Automatic chromosome classification and chromosome abnormalities identification based on dynamic time warping

List of Figures List of Figures Figure 1.1: Major steps of chromosome identification 4 Figure 2.1: Normal human female G-banded karyotype 10 Figure 2.2: Silhouette of a chromosome 10 F

AUTOMATIC CHROMOSOME CLASSIFICATION AND CHROMOSOME ABNORMALITIES IDENTIFICATION BASED ON DYNAMIC TIME WARPING

BENOIT LEGRAND (B E., Supélec, France)

A THESIS SUBMITTED FOR THE DEGREE OF MASTER OF ENGINEERING DEPARTMENT OF ELECTRICAL & COMPUTER ENGINEERING

NATIONAL UNIVERSITY OF SINGAPORE

2006

I would like to thank Mr Seow Hung Cheng, the Power System Lab officer, for his warm welcome and his continuous support

This work was supported by a research scholarship from the National University of Singapore (NUS) I am extremely thankful to NUS for the financial support

My study in Singapore was made possible through the double-degree program between the National University of Singapore (NUS) and Ecole Supérieure d’Electricité (Supélec) I am particularly grateful to the international relation offices of both Supélec and NUS for their advice before and during my stay in Singapore

Table of contents

Table of contents

Acknowledgments i

Table of contents ii

Summary ix

List of publications related to this study xi

List of Tables xii

List of Figures xiii

List of Symbols xv

List of Symbols xv

List of Abbreviations xix

CHAPTER 1 INTRODUCTION 1

1.1 Motivations and objectives for the research 1

1.2 Major steps of chromosome classification 3

1.3 Main contributions 4

1.3.1 Normal chromosome classification 5

1.3.2 Mapping of chromosome abnormalities 6

1.4 Thesis organization 7

CHAPTER 2 CHROMOSOMES AND IMAGING TECHNIQUES 9

2.1 Chromosome structure 9

2.2 Karyotyping and chromosome banding 12

2.3 Chromosome abnormalities 14

2.3.1 Extra or missing chromosomes 14

2.3.2 Duplication 15

Table of contents

2.3.3 Deletion 15

2.3.4 Translocation 16

2.3.5 Inversion 17

2.3.6 Insertion 18

2.3.7 Ring 18

2.4 Modern chromosome imaging techniques 19

2.4.1 Fluorescence in situ hybridization (FISH) 19

2.4.2 Comparative genomic hybridization (CGH) 20

2.4.3 Multicolor karyotyping (M-FISH, SKY and CCK) 21

2.5 Conclusion 22

CHAPTER 3 LITERATURE SURVEY ON AUTOMATIC CHROMOSOME CLASSIFICATION 23

3.1 Image quality improvement 24

3.1.1 Noise removal 24

3.1.2 Debris removal 24

3.1.3 Image enhancement 25

3.2 Chromosome segmentation 26

3.2.1 Threshold 26

3.2.2 Variance 27

3.2.3 Entropy 27

3.3 Touching and overlapping chromosomes 27

3.3.1 Skeleton 28

3.3.2 Curvature 29

3.3.3 Band analysis 29

Table of contents

3.4 Feature extraction 30

3.4.1 Skeleton 30

3.4.2 Length 31

3.4.3 Centromere 31

3.4.4 Density profile 32

3.4.5 Border 32

3.4.6 Area 33

3.4.7 Signature 33

3.5 Feature reduction 33

3.6 Classification 35

3.6.1 Neural Network 36

3.6.1.1 Multi Layer Perceptron 36

3.6.1.2 Probabilistic Neural Networks 37

3.6.1.3 Higher Order Neural Networks 37

3.6.2 Expert system 37

3.6.3 Bayesian classifier 38

3.6.4 Classification in two steps 38

3.6.5 Hybrid method 39

3.7 Implementation 39

3.8 Conclusion 39

CHAPTER 4 CHROMOSOME SEGMENTATION AND FEATURE EXTRACTION 40

4.1 Noise filtering 41

4.2 Segmentation 41

Table of contents

4.2.1 Threshold 42

4.2.2 Border detection 43

4.2.3 Filling 43

4.2.4 Thinning 43

4.2.5 Pruning 44

4.2.6 Skeleton extension 44

4.3 Feature extraction 44

4.3.1 Length 45

4.3.2 Density profile 46

4.3.3 Centromere location 47

4.4 Feature normalization: 48

4.4.1 Length normalization 48

4.4.2 Density profile 48

4.4.3 Centromeric index 50

4.5 Conclusion 51

CHAPTER 5 NORMAL HUMAN CHROMOSOME CLASSIFICATION 52

5.1 Introduction 52

5.2 Data base 53

5.3 DTW-based classifier 55

5.3.1 Motivation for using the DTW algorithm 55

5.3.2 DTW-based chromosome classifier 58

5.3.2.1 Reference chromosomes 60

5.3.2.2 Length feature 62

Table of contents

5.3.2.3 Dimension of the density profile 63

5.3.3 Classification results 64

5.4 Comparison with the Bayesian classifier 66

5.4.1 Bayesian classifier 66

5.4.2 Classification performances 67

5.4.3 Size of the training set 68

5.5 Conclusion 70

CHAPTER 6 ABNORMAL CHROMOSOME ANALYSIS 71

6.1 Introduction 71

6.2 Image acquisition 73

6.2.1 G-Banding images 74

6.2.2 SKY Images 74

6.3 Reference density profiles 75

6.4 Translocation analysis procedure 76

6.4.1 Overall principle 76

6.4.2 Scale ratio 81

6.4.3 Chromosome sections comparison 84

6.4.4 Recombination point identification 88

6.5 Performances and results 89

6.6 Conclusions 95

CHAPTER 7 CONCLUSION 96

Bibliography 98

APPENDIX A IMAGE PROCESSING ALGORITHMS 104

Table of contents

A.1 Median filter 104

A.2 Border follower algorithm 105

A.3 Filling algorithm 107

A.4 Thinning algorithm 108

APPENDIX B DYNAMIC TIME WARPING ALGORITHM 112

APPENDIX C BAYESIAN CLASSIFIER 116

C.1 Theory 116

C.2 Feature dimension reduction 118

APPENDIX D MAPPING OF CHROMOSOME ABNORMALITIES 120

D.1 Cell line HCT116 121

D.2 Cell line HEPG2 123

D.3 Cell line HEP3B 125

APPENDIX E IDEOGRAMS AND REFERENCE DENSITY PROFILES 126

E.1 Chromosome 1 126

E.2 Chromosome 5 127

E.3 Chromosome 6 128

E.4 Chromosome 7 129

E.5 Chromosome 8 130

E.6 Chromosome 10 131

E.7 Chromosome 11 132

E.8 Chromosome 16 133

E.9 Chromosome 17 134

E.10 Chromosome 18 135

Table of contents

E.11 Chromosome 21 136

Summary

Summary

Studies on chromosomes are essential to understand chromosome aberrations and their corresponding diseases Nowadays, human chromosome analysis is commonly used

to identify genetic defects in prenatal screening and in cancer pathology research

Manual chromosome analysis is a slow and laborious operation that requires qualified personnel Modern imaging techniques and image analysis software have recently greatly facilitated the study on chromosome However, there is a lack of software able to deal with abnormal chromosomes and to combine information from different imaging methods This study proposes a procedure to automate the chromosome banding analysis The proposed system is able to automatically map some common chromosome abnormalities by combining a banding analysis with the information provided by the spectral karyotyping imaging techniques The dynamic time warping (DTW) algorithm, traditionally used in speech recognition applications, has been adapted

to the identification of chromosome banding patterns in order to overcome the problems due to the non-rigid nature of chromosomes

At first, a pattern classifier has been developed to perform the classification of normal human chromosomes This classifier is based on the dynamic time warping algorithm in order to be able to compare unknown banding patterns with some pre-computed references This classification method has the main advantage to require only a few training samples in comparison with the traditional chromosome classifiers based on Neural networks or Bayesian classifiers This property is due to the transfer of the

Summary

knowledge that chromosomes can have different elongations from the training set to the classifier itself For the same classification accuracy, the DTW-based classifier achieves a large reduction of 88% of the number of training samples in comparison with the Bayesian classifier This performance is particularly useful for dealing with species that are seldom studied or to classify some abnormal chromosomes with only a few training samples

The second part of the study is dedicated to the development of an automatic system to map abnormal chromosomes Several modern cytogenetics imaging techniques facilitate the study of chromosome defects, but are individually unable to completely map chromosome aberrations Comparative genomic hybridization (CGH) is an imaging method that has the ability to reveal chromosome amplifications and deletions, but not to determine the amplified or deleted sections Multiplex fluorescence in situ hybridization (M-FISH) and spectral karyotyping (SKY) detect inter-chromosomal rearrangements, but are not able to discover intra-chromosomal rearrangement and to identify the exchanged chromosome sections For all these reasons, the traditional banding analysis method is essential to obtain a complete identification of chromosome aberrations Nowadays, banding analysis and combination of information from different imaging systems are done manually by cytogenetics experts This study proposes a procedure to automate the banding analysis operation by using the information given by the spectral karyotyping images The proposed system has been successfully applied to the mapping of chromosome translocations, duplications and deletions in solid tumor cells This new tool

is useful to better understand chromosome disorders

List of publications related to this study

List of publications related to this study

1 B Legrand, C.S Chang, S.H Ong, S.Y Neo and N Palanisamy, “Chromosome classification using dynamic time warping”, submitted to Pattern and Recognition Letters

2 B Legrand, C.S Chang, S.H Ong, S.Y Neo and N Palanisamy, “Mapping of chromosome abnormalities by automated banding analysis”, submitted to IEEE Transactions on Biomedical Engineering

List of Tables

List of Tables

Table 2.1: Description of the seven Denver groups 12 Table 5.1: Normal human chromosome data base 54

Table 5.3: Classification accuracy (DTW-based classifier) 65 Table 5.4: Number of samples in the training and test set 67 Table 5.5: Relation between classification accuracy and features 68 Table 5.6: Classification accuracy (Bayesian classifier) 68

Table 6.3: Translocation t(5;7) in the cell line HCT116 93

List of Figures

List of Figures

Figure 1.1: Major steps of chromosome identification 4 Figure 2.1: Normal human female G-banded karyotype 10 Figure 2.2: Silhouette of a chromosome 10

Figure 2.4: Example of metacentric, submetacentric and acrocentric chromosomes 12 Figure 2.5: Ideograms of the normal human chromosome 16 in different resolutions [50] 14 Figure 2.6: Example of chromosome abnormalities (duplication, deletion, translocation) 17 Figure 2.7: Example of chromosome abnormalities: (inversion, insertion, ring) 19

Figure 2.9: Example of SKY image (cell line HEP3B - liver tumor) 22 Figure 3.1: Major steps of automatic chromosome classification 23 Figure 3.2: Debris on a metaphase image [36] 25 Figure 3.3: Example of touching and overlapping chromosomes on a metaphase image 28 Figure 3.4: Skeleton of overlapping chromosome 29 Figure 3.5: Possible cut points on overlapped chromosomes [13] 29 Figure 4.1: First step: Image quality improvement 41 Figure 4.2: Second step: Chromosome segmentation 41

Figure 4.4: Third step: Feature computation 45 Figure 4.5: Chromosome length computation 45 Figure 4.6: Skeleton between the two sister chromatids 46

Figure 4.8: Normalized density profile of a normal human G-banded chromosome 1 50

Figure 5.1: Normalized density profiles of two normal chromosomes 1 56 Figure 5.2: Alignment found by the DTW algorithm for the density profiles of Figure 5.1 57

List of Figures

Figure 5.3: Normal chromosome DTW-based classifier 58 Figure 5.4: Overall principle of the DTW-based chromosome classifier 59 Figure 5.5: Classification accuracy versus the number of training samples 61 Figure 5.6: Classification accuracy versus the number of pre-selected reference chromosomes 63 Figure 5.7: Classification accuracy versus the dimension of the density profile 64 Figure 5.8: Density profiles of chromosomes 19 and 21 66 Figure 5.9: Classification accuracy versus the number of training samples (Bayesian classifier) 69 Figure 6.1: Necessity of the banding analysis (translocation t(1,11) in the cell line HEP3B) 72 Figure 6.2: Overall flowchart of the proposed algorithm 78 Figure 6.3: Find the normal section that best match an abnormal section 80 Figure 6.4: Comparison of an abnormal section with a reference chromosome 86 Figure 6.5: Similarity between the translocation and sections of a reference chromosome 87 Figure 6.6: Example of translocation t(5;7) from the cell line HCT116 90 Figure 6.7: Section of reference chromosome 5 91 Figure 6.8: Section of reference chromosome 7 92 Figure 6.9: Complete mapping of translocation t(5;7) from the cell line HCT116 94 Figure A.1: Border follower algorithm 105 Figure A.2: Flow chart of the border follower algorithm 106 Figure A.3 Flow chart of the filling algorithm 108 Figure A.4: Flow chart of the thinning algorithm 109 Figure A.5: Flow chart of the first step of the thinning algorithm 111 Figure B.1: Unknown and reference density profiles 112

p ′′ Normalized value of the ith point of the re-sampled density profile

M Number of points in the raw density profiles

N Number of points in the re-sampled density profiles

Chapter 5: Normal human chromosome classification

p

i

L, Normalized length of the ith training sample of chromosome p

p

L Average of the normalized lengths of chromosome p

T Number of training samples used to create each reference template

Chapter 6: Abnormal chromosome analysis

l Length of a normal chromosome in a reference karyotype

lˆ Length of a normal chromosome in the abnormal karyotype

L Average of the normalized lengths of chromosome i

K Total number of normal karyotypes used to compute the average of the

normalized chromosome lengths

List of Symbols

)

,

(

CLR j Chromosome length ratio: Average of the normalized lengths of

chromosome i divided by the average of the normalized lengths of

chromosome j

Γ Scale ratio: Length of a normal chromosome in the reference karyotype

divided by the length of the same normal chromosome in the abnormal karyotype Ratio between the scales of the reference and abnormal karyotypes

i

Γ Scale ratio for chromosome i: Ratio between the scale of the reference

chromosome i and the scale of the abnormal karyotype

R Total number of chromosome used as length patterns to compute the

improved scale ratio

sˆ Length of the abnormal section that are analyzed

i

s Length of the sections of the reference chromosome i that are compared

with the abnormal section

n Abscissa of the beginning of the reference section along the reference

R Density profile sequence of the section of the reference chromosome i

that starts at the abscissa n, in the direction d

)(

DTWCost n d i Result of the DTW comparison, in the direction d, between the

abnormal section and the section of reference chromosome i that starts

List of Symbols

at the abscissa n

( )d

n i Abscissa of the beginning of the best reference section along the

reference chromosome i, for each comparison direction d

nˆ , Best abscissa of the beginning of the reference section along the

reference chromosome i, for the abscissa r of the recombination point

)

(

RPCost r Total DTW cost corresponding to the abscissa r of the recombination

point

rˆ Abscissa of the best recombination point

Appendix B Dynamic time warping algorithm

Unk Sequence of the unknown density profile

r Value of the ith of the reference density profile

n Number of point in the unknown density profile

m Number of point in the reference density profile

List of Symbols

j

C, Cumulative cost in the local DTW matrix

Appendix C Bayesian classifier

N Number of training samples of the classωi

J Criterion for class separability

d Dimension of the feature vector before the dimension reduction process

nd Reduced dimension of the feature vectors

List of Abbreviations

List of Abbreviations

ATCC American type culture collection

CGH Comparative genomic hybridization

CCK Color changing karyotype

FIR Finite Impulse Response (filter)

FISH Fluorescence in situ hybridization

GIS Genome institute of Singapore

ISCN International system for human cytogenetic nomenclature

MLP Multi layer perceptron

M-FISH Multiplex fluorescence in situ hybridization

NUS National university of Singapore

RP Recombination point in an abnormal chromosome

Chapter 1 Introduction

In this introductory chapter, the motivation for the work done is presented Then, major contributions, and structure of the thesis are summarized

1.1 Motivations and objectives for the research

Chromosomes are located in the nuclei of eukaryote cells and contain the DNA double helices They carry the genetic instructions for making living organisms [1] Genetic defects that affect the chromosome structures are numerous, including chromosome rearrangements, duplications and deletions [6] Identification of such chromosome aberrations has enormous impact on clinical diagnosis, medicine development and basic research [56] Nowadays, human chromosome analysis is particularly used to identify genetic disorders in prenatal screening and in cancer pathology research [55]

Chromosome banding analysis is the most commonly used method to check the integrity of chromosomes This method was discovered in 1969 and allows the identification of the chromosomes by assigning a unique banding pattern on each chromosome [3] Banding analysis is routinely used in chromosome classification and abnormalities detection However, manual banding analysis is a slow and laborious operation, and complex chromosome rearrangements are hard to identify As a consequence, there are medical and economic motivations to automate this process [2]

Chapter 1 Introduction

Automatic image analysis software has recently been developed to classify chromosomes by using features including the length and the banding patterns [2] [9] [22] [24] [27] [28] [31] [33] [34] [35] [36] [37] [39] [42] [43] They are commonly used in laboratories to classify normal human chromosomes However, these systems are too task specific and limited to the identification of normal chromosomes of a few species [3] Most of these systems are based on neural networks or Bayesian classifiers because of their ability to learn expert knowledge through a training process [35] These methods require the creation of sizeable training sets for each species

Discovered in the nineties, new cytogenetic imaging techniques have greatly improved the study of chromosome defects [55] [56] These methods are based on in-situ hybridization of the tumor DNA with colored DNA probes They facilitate the detection

of complex chromosome abnormalities However, these methods often require to be combined with a traditional banding analysis to completely identify the various chromosome segments [3] Comparative genomic hybridization (CGH) has the ability to reveal chromosome amplifications and deletions, but not to determine the amplified or deleted sections Multiplex fluorescence in situ hybridization (M-FISH) and spectral karyotyping (SKY) detect inter-chromosomal rearrangements, but are not able to discover intra-chromosomal rearrangement and to identify the exchanged chromosome parts As a consequence, chromosome banding analysis is essential for mapping chromosome aberrations by identifying the involved abnormal sections This issue is currently not addressed by banding analysis imaging software that is mostly unable to

1.2 Major steps of chromosome classification

The process of chromosome classification can be divided in 4 major steps (Figure 1.1) The input of the system is a gray level digital image of a banded chromosome The first 3 steps are image processing operations that are necessary to extract some features from the raw chromosome image The quality of the initial chromosome image and the efficiency of the whole feature extraction process are essential to provide good features to the chromosome identification step This fourth step uses the previously computed features to identify the unknown chromosome If the unknown chromosome is known to

be normal, then the chromosome identification box is a classifier, and its output is one of

Chapter 1 Introduction

the 24 possible chromosome indexes (1, , 22, X, Y) The classifier use a training set to

perform this classification On the contrary, if the unknown chromosome is possibly

abnormal, then additional information about the abnormality is provided to the

chromosome identification box, and its output is a mapping of the abnormal

chromosome This additional information is obtained from a modern color imaging

system, which is able to detect the existence of some abnormalities, but requires an

analysis of the chromosome bands to perform a complete identification

Figure 1.1: Major steps of chromosome identification

Contributions have been made to the steps 2 and 3 of Figure 1.1 in order to perform

an initial efficient feature extraction process with the banded chromosome images

provided by the Genome Institute of Singapore Then, two major contributions are

proposed to the fourth step in order to identify normal and abnormal chromosomes

Firstly, a normal chromosome classifier has been developed in order to strongly reduce

the number of necessary training samples Secondly, a new procedure is proposed to map

Feature extraction

Image quality

improvement

Chromosome segmentation

Chromosome identification

Information about a possible abnormality

Chapter 1 Introduction

abnormal chromosomes by combining the classical banding analysis with information obtained with the Spectral Karyotyping (SKY) system

Chromosomes are non-rigid objects, and their banding patterns are difficult to compare because of their different elongations A section of a chromosome can be more contracted or on the contrary more elongated than the rest of the same chromosome As a consequence, the comparison of chromosome banding patterns with some pre-computed reference patterns is not straightforward because the bands do not line up correctly This study proposes to solve the problem by using the dynamic time warping (DTW) algorithm in order to find an optimum match by stretching the banding patterns DTW systems are well known for their application in automatic speech recognition systems [4]

to compare an unknown word with a reference word that can be pronounced with different speeds DTW algorithm can effectively be applied to the problem of normal chromosome classification by comparing unknown samples with reference patterns The main advantage of this method is that it requires a much smaller training set in comparison with the conventional methods based on Bayesian classifiers or Neural Networks This is because they have to learn from the training samples all the possible elongations that can appear for each chromosome On the contrary, the reference patterns necessary to the DTW-based classifier can be computed from a few training samples In other words, the DTW algorithm transfers the knowledge that chromosomes are non-rigid objects from the training set to the classifier itself In this study, a classifier based on dynamic time warping (DTW) has been developed to perform the classification of human

Chapter 1 Introduction

normal chromosomes In comparison with a Bayesian classifier on the same chromosome data base, a large reduction of 88% of the size of the training set is achieved for the same classification accuracy of 81.0% The reduction of the training set is important to deal with some species that are seldom studied, or to recognize abnormal chromosomes with only a few samples

1.3.2 Mapping of chromosome abnormalities

Different imaging techniques exist to analyze chromosome aberrations Nevertheless, these methods are individually not able to completely map the abnormalities and need to be combined with the traditional banding analysis This study proposes a procedure to automate the banding analysis of abnormal chromosomes by using the information provided by the spectral karyotyping (SKY) imaging methods The SKY images determine the chromosomes involved in inter-chromosomal rearrangements but do not allow the exact identification of the recombined sections On the other hand, banding analysis is able to identify chromosome sections by comparing their bands with the banding patterns of normal chromosomes The comparison process between abnormal and normal chromosome sections is based on the DTW algorithm in order to overcome the problems due to the lack of abnormal training samples and the non-rigid nature of chromosomes The banding analysis is strongly facilitated by using the SKY images to reveal the chromosomes involved in the abnormalities The proposed algorithm has been implemented and successfully applied to the mapping of chromosome translocations, duplications and deletions in solid tumor cells This new procedure aims to be combined with existing cytogenetics software to automatically map abnormal chromosomes

Chapter 1 Introduction

This thesis is organized in seven chapters and presents the work done about chromosome image feature extraction, normal human chromosome classification and chromosome abnormalities identification

At first, Chapter 2 reminds some basics about chromosome structures and abnormal chromosome characteristics The different imaging techniques currently available for chromosome analysis are also presented

In addition, Chapter 3 presents a literature survey on the various methods used in automatic banding analysis and normal chromosome classification

Then, Chapter 4 describes the process of extraction and normalization of the features from the banded chromosome images These features are used later in Chapter 5 and Chapter 6 to identify the chromosomes

Chapter 5 deals with classifiers of normal human chromosomes A new classifier based on dynamic time warping (DTW) is developed and compared with a traditional Bayesian classifier

Chapter 1 Introduction

In Chapter 6, the automatic banding analysis procedure is presented The performances of this new system are evaluated by mapping various abnormal chromosomes coming from solid tumors

Finally, Chapter 7 concludes this study by highlighting the major contributions of this research and its possible applications Possible future research directives are also included

Chapter 2 Chromosomes and imaging techniques

TECHNIQUES

The branch of biology that studies the chromosomes in relation to heredity is called cytogenetics This field is quite recent since the exact number of human chromosomes was found in 1956 This chapter starts with a brief review about chromosomes and banding analysis Then, the most common chromosome abnormalities are presented Finally, the modern chromosome imaging methods are described

Chromosomes are located in the nuclei of eukaryote cells and contain the DNA that carries the genetic instructions for making living organisms [1] [44] There is one DNA double helix per chromosome Normal human cells contain 46 chromosomes (Figure 2.1): 22 pairs of autosome (non-sex) chromosomes, and one pair of sex chromosomes (XX for a female, XY for a male) [16] [33] [35] [41] The two chromosomes in each pair are called homologous chromosomes: one comes from the egg of the mother (maternal homolog) and the other from the sperm of the father (paternal homolog) [42] [52]

Chapter 2 Chromosomes and imaging techniques

Figure 2.1: Normal human female G-banded karyotype

Each chromosome has a constricted region called the centromere The centromere holds together the two sister chromatids (Figure 2.2) before their separation during the mitotic cell division (Figure 2.3) [2] [52]

Figure 2.2: Silhouette of a chromosome

Centromere Telomeres

Sister chromatids

Chapter 2 Chromosomes and imaging techniques

Figure 2.3: Mitotic cell division

On each side of the centromere, are a small and a long arm (Figure 2.4) [1] Chromosomes are metacentric if they have two arms of equal length On the contrary, chromosomes are submetacentric if their centromere is not located in their middle Finally, if the centromere is located at one extremity of the chromosome, the chromosome is acrocentric The two extremities of the chromosomes are called telomeres (Figure 2.2)

Homologous chromosomes

Chapter 2 Chromosomes and imaging techniques

Figure 2.4: Example of metacentric, submetacentric and acrocentric chromosomes

Chromosomes are usually displayed on a karyotype, which is a map of the chromosomes present in the analyzed cell (Figure 2.1) [11] [53] Chromosomes are numbered in order of size, except that 21 is actually smaller than 22 [1] The length and the position of the centromere allow the classification of the chromosomes in 7 groups, called Denver groups (group A to G) (Table 2.1) [5] Banding imaging techniques are required to identify all the chromosomes [42]

Table 2.1: Description of the seven Denver groups

Group Chromosomes Description

A 1,2,3 Longest, metacentric (2 is a little bit submetacentric)

C 6,7,8,9,10,11,12,X Medium size, submetacentric (most difficult to classify)

D 13,14,15 Medium size, acrocentric

E 16,17,18 Small, 16 is metacentric but 17 and 18 are submetacentric

Acrocentric (chromosome 13)

Chapter 2 Chromosomes and imaging techniques

In 1969, the first banding technique was discovered and allowed the classification

of the totality of the 46 chromosomes by assigning a unique banding pattern on each chromosome [3] The observation of chromosomes is done during the metaphase, which

is one of the phases of the cell division by mitosis, just before the splitting process of the chromosomes [2] [11] [15] At this particular moment, chromosomes are fully condensed and easily observable with a microscope

Several banding methods have been developed, but the most commonly used nowadays is the G-banding technique where the bands are produced by staining chromosomes with giemsa stain after a pretreatment with trypsin [1] [3] The dark bands correspond to the part of the double strand DNA rich in adenine and thymine nitrogenous bases

The banding resolution corresponds to the number of bands distinguishable on a complete karyotype Banding resolution can be increased by using more elongated chromosomes, for example chromosome from early metaphase (chromosomes are not completely condensed) rather than metaphase Typical high-resolution banding procedures for normal human chromosomes can resolve a total of 400, 550 or 850 bands per karyotype (Figure 2.5) [7]

The International System for Human Cytogenetic Nomenclature (ISCN) [5] proposes a terminology to identify the chromosome bands The two chromosome arms are labeled p (petit) for the small arm and q (next letter in the alphabet) for the long arm Chromosome arms are divided into regions, which are consistent and distinct morphological features Small arms (p arms) are labeled p1, p2, p3 etc., and long arms (q arms) are labeled q1, q2, q3, etc., counting outwards from the centromere Regions are

Chapter 2 Chromosomes and imaging techniques

divided into bands labeled p11 (one-one, not eleven), p12, p13 etc, sub-bands labeled p11.1, p11.2 etc, and sub-sub-bands for example p11.21, p11.22 [1] At 550 bands, sub-bands are detectable and sub-sub-bands become visible at 850 bands (Figure 2.5)

Figure 2.5: Ideograms of the normal human chromosome 16 in different resolutions [50]

Various chromosome abnormalities could appear on a karyotype [6] The terminology for describing normal and abnormal chromosomes is fixed by the International System for Human Cytogenetic Nomenclature (ISCN) [5] Here is a brief description of some common abnormalities with examples of terminology

Abnormal karyotypes may not have the correct number of chromosomes [11] [16] The extra or missing chromosomes are indicated by respectively + or -:

Chapter 2 Chromosomes and imaging techniques

⇒ 47,XY,+21: Male with 47 instead of 46 chromosomes and the extra chromosome

⇒ 46,XX,del(14)(q23): Female with 46 chromosomes with a deletion of chromosome 14 on the long arm (q) at band 23

Chapter 2 Chromosomes and imaging techniques

2.3.4 Translocation

Chromosome translocations are a genetic defect defined by the interchange of parts between different chromosomes [1] [6] [55] Translocations can be reciprocal (also known as non-Robertsonian) or Robertsonian Reciprocal translocations happen with the exchange of segments between different chromosomes (Figure 2.6 (d)) Robertsonian translocations are the combination of two acrocentric chromosomes around their centromere regions (Figure 2.6 (e)) Moreover, translocations can be balanced or unbalanced Translocations are unbalanced if some parts of the original chromosomes are missing after the incorrect recombination On the contrary, carriers of balanced translocations usually appear healthy because no chromosome segment is missing

⇒ 46,XX,t(2;6)(q35;p21): Reciprocal translocation between chromosome 2 and 6 with the breakpoints located in 2q35 and 6p21

Chapter 2 Chromosomes and imaging techniques

Figure 2.6: Example of chromosome abnormalities: (a) duplication, (b) terminal deletion, (c)

interstitial deletion, (d) Robertsonian translocation and (e) reciprocal translocation

BP: break points in the reference chromosomes RP: recombination points in the abnormal

chromosomes

2.3.5 Inversion

An inversion happens with two breaks in one chromosome [6] The section between the breaks is inverted, and then reinserted within the chromosome (Figure 2.7 (a)) If the inverted section includes the centromere it is called a pericentric inversion On the contrary, it is called a paracentric inversion if the centromere is not involved:

6

c

1

2 3

4

d

BP

RP

Chapter 2 Chromosomes and imaging techniques

⇒ 46,XY,inv(3)(p23q27): Pericentric inversion

⇒ 46,XY,inv(1)(p12p31): Paracentric inversion

2.3.6 Insertion

A chromosome insertion is a rearrangement within a chromosome [5] A section of the chromosome is copied and reinserted within the same chromosome but at a different position (Figure 2.7 (b))

⇒ 46,XX,ins(2)(p13q21q31): Insertion of segment (q21-q31) into a breakpoint at (p13) inside the chromosome 2

2.3.7 Ring

A ring chromosome happens when the two ends of a chromosome stick to each other [1] This often happens after the break of the two extremities of the chromosome (Figure 2.7 (c)) Without their telomere, the new extremities become sticky and stick to each other

⇒ 46,XX,r(7)(p22q36): The end of the short arm (p22) and the end of the long arm (q36) of chromosome 7 are linked to form a ring

Chapter 2 Chromosomes and imaging techniques

Figure 2.7: Example of chromosome abnormalities: (a) inversion, (b) insertion and (c) ring

Fluorescence in situ hybridization (FISH) is a technique used to determine how many copies of a specific segment of DNA are present in a cell [3] [7] [15] A segment of DNA is chemically modified and labeled so that it will look fluorescent under a

5 6

1

2

4 3

5 6

Chapter 2 Chromosomes and imaging techniques

fluorescence microscope This DNA fragment is called a probe, and is hybridized with the chromosome spread in the cell The probe will attach itself to its corresponding segment of DNA in the chromosome spread Consequently, the DNA segment corresponding to the probe will appear fluorescent (Figure 2.8)

Figure 2.8: Example of FISH image

Discovered in 1992, Comparative Genomic Hybridization (CGH) consists in the hybridization of two DNA probes labeled with fluorochrome of different colors [3] [55] [56] One probe is a normal DNA and the other is the sample that needs to be analyzed Differences between the two DNA probes will lead to the predominance of one of the two colors Consequently, CGH is useful to locate amplification or deletion of specific regions However, once an abnormality has been located, the chromosome sections involved in the abnormality have to be identified with other methods like multicolor karyotyping and banding analysis