E-cadherin mutations cell cycle dynamics and morphology
Cátia Inês Durão Ferreira
Thesis to obtain the Master of Science degree in
Biomedical Technologies
Supervisor: Professor João Miguel Raposo Sanches
Examination Committee
Chairperson: Professor Raúl Daniel Lavado Carneiro Martins
Supervisor: Professor João Miguel Raposo Sanches
Vogal: Doctor Maria Raquel Campos Seruca
Vogal: Doctor Nuno Filipe Santos Bernardes
June 2014
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“Keep on sowing your seed, for you never know which will grow - perhaps it all will.”
Albert Einstein
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Acknowledgments
First all I would like to thank my supervisors Prof. João Sanches and Doctor Raquel Seruca for
the all the guidance and support provided for the development of this project.
I would further like to thank Anabela Ferro for the help and availability of data obtained from
Flow Cytometry.
Additionally, I would like to thank Martina Fonseca for the availability to clarify all doubts
regarding the profiling algorithm and its respective code.
To all my friends for their continued support and patience, thank you.
A special thank you to my family, for all their love and encouragement.
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Abstract
E-cadherin is a protein which contributes in the process of cell adhesion and the formation of
organized tissues. Additionally, E-cadherin is involved in transmitting chemical signals within cells,
controlling cell maturation and movement, and regulating the activity of certain genes.
E-cadherin germline mutations are associated with the development of Hereditary Diffuse
Gastric Cancer, an autosomal dominant susceptibility for diffuse gastric cancer.
In this project a framework is employed to assess the potential of fluorescence microscopy as
a predictor of E-cadherin pathogenicity and propensity for HDGC development.
The aim of this work is to collect features that would allow characterization of cells submitted
to mutation of CDH1 gene. These features will be focus in cell cycle dynamics and morphology of
cells.
®
In this study, the images used were acquired by fluorescence microscopy and MATLAB
software was used for image processing and analysis.
The immunofluorescence images used represent preparations from different cellular
populations: Wild-type, Intracellular (T340A, A634V) and Extracellular (R749W, E757K) mutated
populations.
In Total Intensity feature the results allow verifying that only T340A mutated population
present difference comparing with wild-type. A634V population presents an increase of Internuclear
distance with wild type population, although T340A shows a decrease in Internuclear distance,
demonstrating its capacity to form cohesive aggregates, which may be associated with the ability of Ecadherin mutants interact with its direct binding partners. Nuclear area decrease in T340A, R749W
and E757K mutated populations, which could be related to disturbances in signalling pathways. It is
also possible to verify that the cell-cycle dynamics it is not affected by E-cadherin mutations.
Keywords
E-cadherin Mutation, Fluorescence Microscopy, Cell Adhesion; Cell-cycle, Internuclear Distances.
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Resumo
A E-caderina é uma proteína que contribui para o processo de adesão celular e na formação
de tecidos. Adicionalmente ao seu papel na adesão celular, a E-caderina está envolvida na
transmissão de sinais químicos entre células, controlando a maturação e mobilidade celular e
regulando a atividade de alguns genes.
Mutações germinativas da E-caderina estão associados ao desenvolvimento de Carcinoma
Gástrico Difuso Hereditário, uma suscetibilidade autossómica dominante para Carcinoma gástrico
difuso.
Neste projecto é aplicada uma estratégia para aferir o potencial da microscopia de
fluorescência como preditora de patogenicidade da E-caderina e propensão para HDGC.
O objetivo deste trabalho é reunir dados que permitam a caracterização de células
submetidas a mutação do gene CDH1. As características focar-se-ão na dinâmica do ciclo celular e
morfologia das células.
Neste estudo, as imagens utilizadas foram adquiridas por Microscopia de Fluorescência e foi
usado o software MATLAB ® para executar o processamento e análise de imagem.
Estas imagens representam diferentes populações, Wild-type e populações intracelular
(T340A, A634V) e extracelular (R749W, E757K) mutadas.
Com a característica Intensidade total, os resultados permitem verificar que apenas a
população mutada T340A apresenta diferenças comparando com Wild-type. A população A634V
apresenta um aumento da distância internuclear com população de Wild-type, e T340A mostra uma
diminuição na distância Internuclear, demonstrando a sua capacidade para formar agregados
coesivos, que podem ser associados com a capacidade de mutantes de E-caderina interagir com os
seus parceiros de ligação direta. A área nuclear diminui nas populações mutadas T340A, R749W e
E757K, o que poderá estar relacionado a distúrbios em vias de sinalização. Também é possível
verificar que a dinâmica do ciclo celular que não é afectada por mutações da E-caderina, de modo
semelhante aos resultados obtidos por citometria de fluxo.
Palavras-chave
Mutação da E-cadherin, Microscopia de Fluorescência, Adesão Celular; Ciclo celular, Distância
Internuclear.
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Index
Contents
Acknowledgments ....................................................................................................................................v
Abstract................................................................................................................................................... vii
Resumo ................................................................................................................................................... ix
Index ........................................................................................................................................................ xi
1. Introduction .......................................................................................................................................... 2
1.1. Motivation ...................................................................................................................................... 2
1.2. Problem Statement ....................................................................................................................... 2
1.3. Object of Study ............................................................................................................................. 2
1.4. Objectives ..................................................................................................................................... 2
2. Review of the Literature....................................................................................................................... 6
2.1 E-cadherin ...................................................................................................................................... 6
2.2. Function of E- cadherin ................................................................................................................. 6
2.3. Structure of E-cadherin ................................................................................................................. 6
2.4. Adherens Junction ........................................................................................................................ 6
2.5. Hereditary Diffuse Gastric Cancer ................................................................................................ 7
2.6. Pathogenicity of E-cadherin .......................................................................................................... 8
2.7. Mutation of E-cadherin .................................................................................................................. 8
2.8. Cell Cycle ...................................................................................................................................... 8
2.9. Polarity ........................................................................................................................................ 10
2.10. Chromatin Condensation .......................................................................................................... 11
2.11. Flow Cytometry ......................................................................................................................... 11
2.12. Microscopy ................................................................................................................................ 12
2.13. Fluorescence Microscopy ......................................................................................................... 12
2.14. Immunofluorescence................................................................................................................. 13
2.15. Flow Cytometry vs. Fluorescence Microscopy ......................................................................... 14
2.16. DAPI .......................................................................................................................................... 14
3. Materials and Methods ...................................................................................................................... 16
3.1. Cell cultures ................................................................................................................................ 16
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3.2. Bioinformatic processing ............................................................................................................. 17
3.2.1. Image Enhancement ............................................................................................................ 18
3.2.2. Processing............................................................................................................................ 18
3.2.2.1. Image Threshold ............................................................................................................... 18
3.2.2.2. Watershed ......................................................................................................................... 19
3.2.2.3. Segmentation .................................................................................................................... 19
3.2.3. Features ............................................................................................................................... 20
3.2.3.1. Shape Measurements ....................................................................................................... 20
3.2.3.2. Pixel Value Measurements ............................................................................................... 20
3.2.4. Machine learning technique ................................................................................................. 21
3.2.4.1. K-means ............................................................................................................................ 21
3.2.4.2. Measurement of Internuclear Distance ............................................................................. 21
3.2.5. Statistical Tests .................................................................................................................... 22
3.2.5.1. Chi-square variance test ..................................................................................................... 22
3.2.5.2. Two-sample t-test for equal means and equal but unknown variances ............................ 22
4. Results ............................................................................................................................................... 24
4.1. Total Intensity.............................................................................................................................. 24
4.2. Internuclear distance................................................................................................................... 26
4.3. Nuclear area ............................................................................................................................... 30
5. Discussion ......................................................................................................................................... 36
Total Intensity ..................................................................................................................................... 36
Internuclear Distance ......................................................................................................................... 36
Nuclear Area ...................................................................................................................................... 37
Cell Cycle ........................................................................................................................................... 38
6. Conclusion and Future perspectives ................................................................................................. 40
Bibliography ........................................................................................................................................... 41
Appendix A ............................................................................................................................................ 45
Appendix B ............................................................................................................................................ 51
Appendix C ............................................................................................................................................ 56
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List of figures
Figure 1 - The Cadherin-catenin complex.. ............................................................................................. 7
Figure 2 - Phases of Cell-cycle and cell division. .................................................................................... 9
Figure 3 - DNA content. ......................................................................................................................... 10
Figure 4 - Diagram of workings of cytometer. ....................................................................................... 12
Figure 5 - Schematic of indirect detection method ................................................................................ 13
Figure 6 - Structure of the E-cadherin gene and protein. ...................................................................... 17
Figure 7- Overview of the Cell Nuclei Segmentation and Labelling Procedure. ................................... 18
Figure 8 - a) DNA content profile of a cell. Histograms of Total Intensity of b) Wild-type c) T340A
mutated d) A634V mutated e) R749W mutated and f) E757K mutated populations ............................ 24
Figure 9 - Correspondence of nuclei with cell cycle phase ................................................................... 28
Figure 10 - Internuclear Distances between nuclei with same cell-phase ............................................ 28
Figure 11 - Plots Total Intensity vs. Nuclear Area ................................................................................. 34
Figure 12 - Percentage of cell cycle phase, obtained by Flow Cytometry. ........................................... 38
Figure 13 - Correspondence of nuclei with cell-phase of Wild-type 0003 population ........................... 46
Figure 14 - Correspondence of nuclei with cell-phase of Wild-type 0009 population ........................... 46
Figure 17 - Correspondence of nuclei with cell-phase of Wild-type 0038 population ........................... 47
Figure 33 - Image with Correspondence of nuclei with cell-phase of mutated 2245-0048 population . 54
Figure 35 - Image with Correspondence of nuclei with cell-phase of mutated 2269-0057 population . 55
Figure 37 - Plots Total Intensity vs. Area of A634V population ............................................................. 57
Figure 39 - Plots Total Intensity vs. Area of E757K population ............................................................. 58
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List of Tables
Table 1 - Details from CDH1 germline mutations. ................................................................................. 16
Table 2 – Bioimaging pipeline applied in the analysis of fluorescence images used in this study. ...... 22
Table 3 - T-Test for Total Intensity. Statistical significance was consider for p<0.05. .......................... 25
Table 4 - Mean of Total Intensity of each population. ........................................................................... 25
Table 5 - t-test for Internuclear Distance between Wild-type and each population. p<0.05. ................ 26
Table 6 - Mean of internuclear distance of each population. ................................................................ 26
Table 7 - Chi-square test Internuclear Distance Variance between Wild-type and each population.
p<0.05.. .................................................................................................................................................. 27
Table 8 - Variance of internuclear distance of each population. ........................................................... 27
Table 9 - T-test to Internuclear Distance to each sub-population. p<0.05 ............................................ 29
Table 10 - Mean of Internuclear Distance of each sub-population. ...................................................... 30
Table 11 - t-test to nuclear area. p<0.05. .............................................................................................. 30
Table 12 - Mean of Nuclear Area of each population. ........................................................................... 31
Table 13 - Chi-square test to Variance of nuclear area. p<0.05. .......................................................... 31
Table 14 - Variance of Nuclear Area of each population. ..................................................................... 31
Table 15 - T-Test to nuclear area of each subpopulation. p<0.05. ....................................................... 32
Table 16 - Mean of Nuclear Area of each sub-population. p<0.05. ...................................................... 33
Table 17 - Percentage of nuclei in each phase. “Other” represents the aneuploid cells identified in the
immunofluorescence images. ................................................................................................................ 34
Table 18 - t-test to percentage nuclei in each phase. p=0.05 ............................................................... 34
xiv
1
Introduction
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1. Introduction
1.1. Motivation
The CDH1 gene provides instructions for making a protein called epithelial cadherin or Ecadherin. This protein is found at the membrane of epithelial cells, which are the cells that line the
surfaces and cavities of the body. E-cadherin belongs to a family of proteins called cadherins whose
function is to promote cell-cell adhesion and form organized tissues. In addition to its role in cell
adhesion, E-cadherin is involved in transmitting biochemical signals within cells, controlling cell
maturation and movement. E-cadherin and its associated cytoplasmic proteins have a role as
tumour/invasion suppressor, preventing cells from invading the neighbouring tissues [1, 2].
CDH1 mutations cause only 1-3% of all gastric cancers and in families with a strong history of
diffuse gastric cancer, only one-third to one-half are due to CDH1 mutations. CDH1 germline
mutations are associated with the development of Hereditary Diffuse Gastric Cancer (HDGC) [3].
HDGC is autosomal dominant susceptibility for diffuse gastric cancer, a poorly differentiated
adenocarcinoma that infiltrates into the stomach wall causing thickening of the wall without forming a
distinct mass. Diffuse gastric cancer is also referred to as signet ring carcinoma or isolated cell-type
carcinoma. The average age of onset of HDGC is 38 years, with a range of 14-69 years. The majority
of the cancers in individuals with a CDH1 mutation occur before the age of 40 years. The estimated
cumulative risk of gastric cancer by age 80 years is 80% for both men and women [4].
Women with HDGC also have an elevated risk of breast cancer, predominantly of the lobular
type, with a 20-40% lifetime risk. Most of these women are over 50 at diagnosis. Mutations in the Ecadherin gene have been identified in some families and genetic testing is now available.
1.2. Problem Statement
Diffuse gastric cancer harbour a particular cytologic phenotype. Cancer cells are isolated with
an eccentric nucleus and the proliferation rate of such tumours cells remain to be established.
1.3. Object of Study
This study will be developed based on two groups of images collected through fluorescence
microscopy. Both groups are stable cell lines from Chinese Hamster Ovary (CHO). One of the groups
represents Wild-type CDH1 and the other group contains cells stably expressing proteins transfected
with diverse CDH1 mutants. For labelling of cells, two markers were used, the E-cadherin antibody
and DAPI for nuclei labelling.
1.4. Objectives
Since the group had access to a set of derived HDGC patients we decided to analyse the
morphologic and cell cycle changes related to distinct CDH1 missense mutations.
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The collection of information is done by a computational tool that allows, in a semi-automatic
manner, the selection of cells of interest, and calculating morphological and texture parameters.
The data extracted from analysis of images in study intends to determine whether there is
change in cell cycle dynamics and morphology of cells harbouring E-cadherin mutations.
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4
2
Review of the Literature
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2. Review of the Literature
2.1 E-cadherin
Cadherins comprise a large family of transmembrane or membrane-associated glycoproteins
that mediate specific cell-cell adhesion in a Calcium-dependent manner, functioning as key molecules
in the morphogenesis of a variety of organs [5, 6].
E-cadherin was first identified in 1977 by Takeichi. This single-pass transmembrane
glycoprotein is encoded by the CDH1 gene, annotated to the human chromosome 16q22.1 [7, 8].
2.2. Function of E- cadherin
One of the most important and ubiquitous types of adhesive interactions required for the
maintenance of solid tissues is that mediated by the classic cadherin adhesion molecules. Cadherins
are well known to play important roles in cell recognition and cell sorting during development.
Nevertheless, cadherins continue to be expressed at high levels in virtually all epithelial tissues with
an anti-invasive role. E-cadherin is required for cells to remain tightly associated in the epithelium, and
in its absence cell adhesion is abrogated. Moreover, E-cadherin is thought to act as an important
tumour suppressor [9].
2.3. Structure of E-cadherin
Classical cadherins are composed by an extracellular portion that contains five homologous
segments called cadherin domains, a transmembrane segment, and a highly conserved cytoplasmic
domain of ~170 amino acids [10].
2.4. Adherens Junction
E-Cadherin dimerizes and forms trans-homophilic interactions with neighbouring proteins to
form cadherin clusters. Ca2+ ions are required to stiffen the extracellular domain and are essential to
form homophilic interactions. The E-cadherin intracellular domain contains binding sites for the
catenins p120 and β-catenin, thereby forming the cadherin–catenin complex. E-Cadherin is stabilized
by p120 catenin at the cell membrane preventing its endocytosis and further degradation. β-Catenin
binds α-catenin, which in turn binds actin and several actin-associated proteins. The cadherin–catenin
complex also binds many other proteins, including signalling proteins, and cell surface receptors and
forms a hub for protein–protein interactions (Figure1) [11].
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Figure 1 - The Cadherin-catenin complex. Adapted from Baum et al, 2011 [11].
2.5. Hereditary Diffuse Gastric Cancer
Gastric cancer is the second most common cause of cancer death worldwide. Two major
histopathological variants of this cancer have been described: an intestinal type and a diffuse type. A
decline in the overall incidence of gastric cancer can be attributed primarily to a decrease of the
intestinal variant of gastric cancer. The incidence of the diffuse type has remain constant [12].
The histopathologic appearance of diffuse gastric cancer specimens reveals a pattern of
isolated, mucin filled tumour cells within the wall of the stomach. Decreased expression of the Ecadherin gene in cases of diffuse gastric cancer may account for morphological differences between
intestinal and diffuse variants. Epithelial cadherin is a transmembrane glycoprotein and plays a major
role in epithelial architecture, cell adhesion, and suppressor. CDH1 was first associated with gastric
cancer when somatic mutations were identified in diffuse gastric cancer specimens. Since then,
germline mutations in CDH1 have been found in families with autosomal dominant susceptibility to
hereditary diffuse gastric cancer (HDGC) [13].
An autosomal dominant cancer susceptibility syndrome, HDGC has an average age of onset
of 38 years for clinically detectable diffuse gastric cancer. Germline mutations in CDH1 are found in
30% to 40% of clinically defined families with HDGC from different ethnic backgrounds, predominantly
from low incidence populations The CDH1 mutation spectrum is heterogeneous and includes point
mutations, small deletions, and insertions distributed along the entire coding sequence. The
identification of CDH1 mutations offers the opportunity of cancer risk-reduction strategies for
unaffected at-risk individuals using highly invasive methods, namely prophylactic gastrectomy. Along
with a risk of diffuse gastric cancer, there is an excess of lobular breast cancer in families with
clinically defined HDGC. This is not unexpected because loss of CDH1 expression is a cardinal
feature of lobular breast cancer and HDGC and both somatic CDH1 mutations and promoter
7
hypermethylation are found frequently in lobular breast cancer but only rarely in infiltrating ductal
carcinoma [13].
2.6. Pathogenicity of E-cadherin
The cells expressing pathogenic CDH1 missense mutations fail to aggregate and become
more invasive, in comparison with cells expressing wild-type E-cadherin, supporting their pathogenic
relevance. E-cadherin expression and stability at the cell surface of epithelial cells are tightly regulated
by post-translational mechanisms, including exocytosis and endocytosis. This mechanisms organize
E-cadherin transport to adherens junctions, internalization, recycling, sequestration and degradation.
The E-cadherin cytoplasmic domain is the main actor in processes above mentioned. Once
synthesized E-cadherin is transported from the Golgi apparatus to the plasma membrane (PM), after
the association of β-catenin and Type Ig phosphatidylinositol phosphate kinase to the cytoplasmic tail
of E-cadherin. At the PM, p120-catenin bind to the cadherin juxtamembrane domain, stabilizing and
preventing the degradation endocytic pathways of E-cadherin. E-cadherin deprived of p120 is prone to
interact with other proteins, such as clathrin adapter proteins and Hakai, promoting E-cadherin
internalization. After internalization, E-cadherin can be recycled back to the PM or targeted for
degradation [14].
2.7. Mutation of E-cadherin
In normal tissues, E-cadherin plays a powerful tumour suppressor role and the assembly and
maintenance of cadherin-catenins interactions is tightly regulated. E-cadherin expression is partially or
completely loss in various types of cancer and this is associated to increased cell invasion and
metastatic potential. In sporadic diffuse gastric cancer, E-cadherin loss is associated with somatic
mutations,
loss
of
heterozygosity,
promoter
hypermethylation,
aberrant
glycosylation
and
overexpression of transcriptional repressors, but these mechanisms explain only a rather limited
percentage of cases with loss of E-cadherin [15].
Cells expressing mutated E-cadherin show an altered morphology, decreased calciumdependent cell aggregation, increased cell motility and changes in actin filament organization [16].
2.8. Cell Cycle
The cell cycle is a ubiquitous, complex process involved in the growth and proliferation of
cells, organism development, regulation of DNA damage repair, tissue hyperplasia as a response to
injury, and diseases such as cancer. The cell cycle involves numerous regulatory proteins that direct
the cell through a specific sequence of events culminating in mitosis and the production of two
daughter cells [17].
Cell cycle progression is divided into phases G1, S, G2 (collectively called interphase) and M
(mitosis). Once a cell takes birth from its parent cells by division, it enters a Gap phase, in case the
cell is committed not to divide further it enters a resting state, marked by G 0, if it does have a fate that
8
entails division however, it stays in the G 1 phase, where it tends to perform its normal cellular role and
also undergoes cellular growth, including production of organelles and duplication of materials with the
exception of chromosomes. This is then followed by the S phase, where the DNA in a cell is
duplicated, this is then followed by the G2 phase where the duplicated DNA is checked for errors and
suitably repaired by DNA repair pathways. This is then followed by the Mitotic or the M phase where
actual cell division takes place, the daughter cell then enters the G 0 or G1 phase and the cycle
continues and so on and so forth. G0, G1, S and G2 can all be collectively called the Interphase [18].
Figure 2 - Phases of Cell-cycle and cell division.
Adapted from Morgan, 2007. [19]
When DNA content is measured in a large population of cells and the data plotted in a DNA
content frequency histogram, G1 and G2/M phase cells create peaks at DNA index (DI) = 1.0 and 2.0,
respectively. S-phase cells are distributed in between the peaks [20].
The division of all cells must be carefully regulated and coordinated with both cell growth and
DNA replication in order to ensure the formation of progeny cells containing intact genomes. In
eukaryotic cells, progression through the cell cycle is controlled by a series of protein kinases that
have been conserved from yeasts to mammals. In higher eukaryotes, this cell cycle machinery is itself
regulated by the growth factors that control cell proliferation, allowing the division of individual cells to
be coordinated with the needs of the organism as a whole. Not surprisingly, defects in cell cycle
regulation are a common cause of the abnormal proliferation of cancer cells, so studies of the cell
cycle and cancer have become closely interconnected, similar to the relationship between studies of
cancer and the cell signalling pathways [21].
Cells at different stages of the cell cycle can also be distinguished by their DNA content. For
example, animal cells in G1 are diploid (containing two copies of each chromosome), so their DNA
content is referred to as 2n (n designates the haploid DNA content of the genome). During S phase,
replication increases the DNA content of the cell from 2n to 4n, so cells in S have DNA contents
ranging from 2n to 4n. DNA content then remains at 4n for cells in G2 and M, decreasing to 2n after
cytokinesis. Experimentally, cellular DNA content can be determined by incubation of cells with a
fluorescent dye that binds to DNA, followed by analysis of the fluorescence intensity of individual cells
in a flow cytometer or fluorescence-activated cell sorter, thereby distinguishing cells in the G1, S, and
G2/M phases of the cell cycle (see Figure 3) [21].
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Figure 3 - DNA content.
Source: Cooper, 2006. [21]
In G1 phase, actin cables are evenly distributed throughout the cytoplasm, resulting in uniform
deposition, isotropic growth, and spherical cell morphology. As cells transition into S phase, the actin
cytoskeleton becomes polarized, and vesicle deposition occurs apically at the site of bud emergence.
After initial bud emergence, growth remains limited to the developing daughter cell, but becomes
isotropic to create a spherical bud. After anaphase and nuclear division, the actin cytoskeleton
reorients toward the site of division to enable cytokinesis [22].
The capacity to grow is higher in anaphase- and G1-arrested cells than cells arrested in S
phase and early mitosis. Unexpectedly, pheromone treatment of G 1-arrested cells overrides this
capacity for extended growth. This growth limitation by pheromone and at the G 1/S transition is
mediated by actin polarization; many genes involved in protein synthesis are down-regulated in
response to pheromone treatment. Polarization of the actin cytoskeleton in general is sufficient to limit
protein synthesis and growth in all cell cycle stages examined. The growth varies during an
unperturbed cell cycle slowing at the time of polarized growth [22].
2.9. Polarity
Cell polarity is implicated in differentiation, proliferation and morphogenesis of unicellular and
multicellular organisms. Cell polarity relies on the asymmetric organization of cellular components and
structures, and the establishment and maintenance of cell polarity involves many processes including
signalling cascades, membrane trafficking events and cytoskeletal dynamics, all of which need to be
coordinated in a highly regulated manner. Dysregulation of cell polarity are associated with
developmental disorders and cancer [23].
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2.10. Chromatin Condensation
It is a generally accepted that genetic information encoded process DNA exists and functions
within the context of chromatin. Chromatin organization is dynamic, and changes in chromatin
structure can either facilitate or inhibit DNA accessibility. The packaging of DNA into chromatin
presents a significant challenge to essential cellular processes such as transcription, DNA replication
and repair, and chromosome segregation [24].
The level of chromatin condensation is related to the silencing/activation of chromosomal
territories and therefore impacts on gene expression. Chromatin condensation changes during cell
cycle, progression and differentiation, and is influenced by various physicochemical and epigenetic
factors [25].
2.11. Flow Cytometry
Since the initial commercialization of Flow Cytometry (FC) and Fluorescence Activated Cell
Sorting (FACS) in 1968, they have undergone significant improvement [26]
Flow cytometry allows the simultaneous measurement of multiple fluorescent emissions and
light scatter induced by illumination of single cells or microscopic particles in suspension, as they flow
rapidly through a sensing area. In some systems, cell sorters, individual cells or particles may be
physically separated according to their properties. Thus, FC is unique in that multiple biological
parameters can be quantised simultaneously on a single-particle basis, while up to thousands of
events per second may be examined. As a result, large and heterogeneous cell populations are
described based on the biometric properties of their individuals [27].
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. Additionally,
antibodies conjugated to fluorescent dyes can bind specific proteins on cell membranes or inside cells.
When labelled 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 allows several cell properties to be measured simultaneously. Commonly used
dyes include propidium iodide, phycoerythrin, and fluorescein, although many other dyes are available
[28].
11
Figure 4 - Diagram of workings of cytometer.
Source: Rowley, 2012. [29]
2.12. Microscopy
The accurate determination of cell morphology is critical to many aspects of biomedicine. For
instance, histopathology can indicate the stage of cancer based on the organization and shape of cells
present in a patient biopsy; yet, it still relies on experienced physicians to visually recognize the
qualitative differences in cell phenotype. If morphological analysis could be performed quantitatively,
the greater potential to reveal subtle disparities in cell phenotype could radically improve the way we
grade cancer. Meanwhile, the drug screening industry has actively adopted computer-guided
morphological assessment to uncover the potential of new drugs. High content screening platforms
allow us to gain access to rich phenotypic information that can be quantitatively analyzed and
statistically distinguished, but the priority of existing platforms is to foster the speed of image
processing, so some measurement resolution is often conceded. Therefore, an advance in technology
that improves our ability to rapidly and accurately quantify cell morphology can greatly impact the
biomedical community [30].
2.13. Fluorescence Microscopy
The term fluorescence was coined by George Gabriel Stokes [31]. It is one form of a quantum
mechanical process called photoluminescence, which becomes visible as optical radiation during the
relaxation of a molecule from an excited (higher energy) state to its (lower energy) ground state
accompanied by the emission of a photon. Depending on the duration of the phenomenon (lifetime of
-9
-6s
the excited state), one distinguishes between fluorescence (~ 10 -10 ) and phosphorescence (~ 10
-
3
-1000 s) [32].
The possibility of studying the distribution and morphology of autofluorescent structures in
biological specimens was recognized by Köhler, in 1904, during early experiments in ultra-violet
microscopy [33].
The technique of fluorescence microscopy (FM) has become an essential tool in biology and
the biomedical sciences, as well as in materials science due to attributes that are not readily available
12
in other contrast modes with traditional optical microscopy. The application of an array of
fluorochromes has made it possible to identify cells and sub-microscopic cellular components with a
high degree of specificity amid non-fluorescing material. In fact, the fluorescence microscope is
capable of revealing the presence of a single molecule. Through the use of multiple fluorescences
labelling, different probes can simultaneously identify several target molecules simultaneously [34].
A variety of specimens exhibit autofluorescence, without the application of fluorochromes,
when they are irradiated, a phenomenon that has been thoroughly exploited in the fields of botany,
petrology, and the semiconductor industry. In contrast, the study of animal tissues and pathogens is
often complicated with either extremely faint or bright, nonspecific autofluorescence. Of far greater
value for the latter studies are added fluorochromes, also termed fluorophores, which are excited by
specific wavelengths of irradiating light and emit light of defined and useful intensity. Fluorochromes
are stains that attach themselves to visible or sub-visible structures, are often highly specific in their
attachment targeting, and have a significant quantum yield (the ratio of photon absorption to
emission). The widespread growth in the utilization of fluorescence microscopy is closely linked to the
development of new synthetic and naturally occurring fluorophores with known intensity profiles of
excitation and emission, along with well-understood biological targets [34].
2.14. Immunofluorescence
Immunofluorescence (IF) is a common laboratory technique used in almost all aspects of
biology. This technique based on pioneering work by Coons and Kaplan, and later by Mary Osborne,
has been widely used both in research and clinical diagnostics. Applications include the evaluation of
cells in suspension, cell cultures, tissue, beads and microarrays for the detection of specific proteins
[35].
In this method is used an unlabelled primary antibody that is specific for the protein of interest.
A secondary antibody that specifically detects a constant portion of the first antibody is used and is
tagged with the fluorescent dye that will allow the indirect visualization of the target protein of cells
(see Figure 5) [35].
Figure 5 - Schematic of indirect detection method
Source: http://www.piercenet.com/method/immunodetection-ihc
13
The expression of the variant of interest was attained through transfection into CHO cells
negative for E-cadherin of a vector containing the respective site-directed mutagenesis altered CDH1
gene. The data used within this study consisted of a set of fluorescence microscopy images from
these cell cultures.
The primary mouse monoclonal anti-E-cadherin has affinity with E-cadherin, and when applied
to cell cultures preparations the primary antibody and E-cadherin bind. To reveal the protein of interest
in FM it is necessary to bind a second antibody. In this study it was used the Alexa Fluor 488 goat
anti-mouse antibody, which conferred a green tonality to E-cadherin.
2.15. Flow Cytometry vs. Fluorescence Microscopy
The main advantage of revealing fluorescence by FC consists in the possibility to perform a
large number of measures in the test sample, in an objective, rapid, and reproducible manner. It is
also current opinion that FC is more sensitive than FM, at least when the latter relies on human eye for
fluorescence detection. In addition, the duration of observation in FM is necessarily longer than in FC,
thus possibly contributing to neglect a fraction of positive cells due to fluorescence bleaching. On the
other hand, the great disadvantage of FC is the fact that it cannot directly recognize the structures
emitting fluorescence, unlike in optical microscopy. This limitation is only partially overcome by the
gating procedure, i.e. by drawing boundaries around subsets of events in data plots. For instance,
gating clusters of events in the FSC/SSC dot plots may be a rough procedure especially when a
complex and heterogeneous sample, such as seminal fluid, is examined [36].
2.16. DAPI
The blue-fluorescent DAPI, 4',6-diamidino-2-phenylindole, nucleic acid stain preferentially
stains double stranded DNA; it appears to associate with AT clusters in the minor groove. Binding of
DAPI to double stranded DNA produces a ~20-fold fluorescence enhancement, apparently due to the
displacement of water molecules from both DAPI and the minor groove. DAPI also binds RNA,
however in a different binding mode - one thought to involve AU-selective intercalation. The
DAPI/RNA complex exhibits a longer-wavelength fluorescence emission maximum than the
DAPI/double stranded DNA complex (~500 nm versus ~460 nm) and a quantum yield that is only
about 20% as high.
DAPI is a popular nuclear counterstain for use in multicolour fluorescent techniques. Its blue
fluorescence stands out in vivid contrast to green, yellow, or red fluorescent probes of other structures.
The counterstaining protocols are compatible with a wide range of cytological labelling techniquesdirect or indirect antibody-based detection methods, mRNA in situ hybridization, or staining with
fluorescent reagents specific for cellular structures. DAPI can also serve to fluorescently label cells for
analysis in multicolour flow cytometry experiments [37].
14
3
Materials and Methods
15
3. Materials and Methods
3.1. Cell cultures
In this study we used images acquired with fluorescence microscopy, a technique described
before. These images were obtained from CHO cultures and represent two distinct cellular
populations, one category was obtain from wild-type cells, and others were obtained from cells
expressing mutant E-cadherin, as described before [14].
For that purpose, stably CHO cells expressing E-cadherin were seeded on glass coverslips in
6-well plates and grown to at least 80% confluence. Fixation was then performed in ice-cold methanol
for 20 minutes, followed by washing and 30 minute blocking in 5% Bovine Serum Albumin (BSA) and
Phosphate Buffered Saline (PBS), at room temperature. E-cadherin was then tagged by indirect
immunofluorescence with Alexa Fluor 488 (green). E-cadherin was then tagged with a primary mouse
monoclonal E-cadherin antibody (BD Biosciences) diluted at 1:300 in PBS with 5% BSA, and
incubated for 1 hour at room temperature. The Alexa Fluor 488 goat anti-mouse was used as
secondary antibody and was diluted 1:500 (5% BSA/PBS) and incubated for another hour at room
temperature and in the dark. The coverslips were mounted on slides using mounting media
(Vectashield with DAPI), in order to visualize the cell nuclei, in this case with a blue coloration.
Fluorescent microscopic images were acquired with an Apotome Axiovert 200M Fluorescence
Microscope Zeiss, with 40x objectives and fixed illumination. Images were captured through the
Axiocam HRm camera and subsequently processed with the software Zeiss Axion Vision 4.8.
The images dataset used for further analysis comprises six images from wild-type cultures and
seven images from CHO cultures expressing mutant E-cadherin. The mutations herein analyzed are
located in the extracellular and intracellular regions of E-cadherin (see Table 1).
Details from CDH1 germline mutations described to date in familial gastric cancer
Domain
Extracellular
CDH1
Amino
mutation
acid
1018 A>G
T340A
Pathogenicity
Setting
Age
Intracellular
A634V
References
onset
Yes
Hereditary
47/?
/familial
1901 C>T
Ancestry
Yes
Hereditary
35/30
Europe
Oliveira et al, 2002;
(I)/China (I)
Zhang et al, 2006
UK(I)/New
Suriano 2003;
Zealand
More , 2007;
(I)/Portugal (I)
Kaurah, 2007
2245 C>T
R749W
Yes
Hereditary
36-49
Colombia (I)
Kaurah, 2007
2269 G>A
E757K
Yes
Hereditary
38
Portugal(I)
Simões-Correia, 2008
Table 1 - Details from CDH1 germline mutations.
Adapted from: Corso, 2013. [38]
16
Figure 6 - Structure of the E-cadherin gene and protein.
Adapted from: Oliveira, 2006
3.2. Bioinformatic processing
®
The software applied in this study was MATLAB , this software was used to develop an
algorithm that allows detaching the nucleus from cytoplasm, by segmentation, and extracting
properties that enable morphologic and texture characterization.
®
MATLAB is an interactive system for numerical computation, visualization, and programming.
Using MATLAB, it is possible to analyze data, develop algorithms, and create models and
applications. The language, tools, and built-in math functions enable you to explore multiple
approaches and reach a solution faster than with spreadsheets or traditional programming languages,
™
such as C/C++ or Java . MATLAB can be used for a range of applications, including signal
processing and communications, image and video processing, control systems, test and
measurement, computational finance, and computational biology [39].
The images acquire through fluorescence microscope were submitted to image processing in
order to obtain quantitative features through bioimaging analysis. Therefore, all images were analysed
according to procedure briefly described in Figure 7 [40].
17
Figure 7- Overview of the Cell Nuclei Segmentation and Labelling Procedure.
3.2.1. Image Enhancement
Image enhancement techniques allow the increase of the signal-to-noise ratio and extract
image features by modifying the colours or intensities of an image [39].
In this study the images were submitted to Denoising. The aim of denoising is to remove noise
and/or spurious details from a given possibly corrupted digital picture while maintaining essential
features such as edges [41]. For this we used the function fspecial that creates a two-dimensional
Gaussian lowpass filter. The fspecial function returns a correlation kernel, which is the appropriate
form to use with imfilter tool. [39].
3.2.2. Processing
The Image Processing Toolbox™ allows performing image analysis, image segmentation,
geometric transformations, and image registration [39].
3.2.2.1. Image Threshold
Image thresholding is a simple, way of partitioning an image into a foreground and
background. This image analysis technique is a type of image segmentation that isolates objects by
converting grayscale images into binary images. [39].
Common image thresholding algorithms include histogram, level, and Ostu’s methods [39].
Otsu’s method selects the threshold by minimizing the within-class variance of the two groups
of pixels separated by the thresholding operator. It does not depend on modelling the probability
density functions; however, it assumes a bimodal distribution of gray-level values [42].
18
The im2bw function produces binary images from indexed, intensity, or RGB images. To do
this, it converts the input image to grayscale format, and then converts this grayscale image to binary
by thresholding. [39].
Morphological operators
In order to minimize the consequent imperfections of thresholding procedure, we used
morphological operators to form and structure of the image. For these were used several functions
described as follows:
- to find connected components in binary image specifying the desired connectivity for the
connected components we used bwconncomp function;
- the imfill function was used to fill image regions and holes. A hole is a set of background pixels
that cannot be reached by filling in the background from the edge of the image;
- to perform morphological opening on the grayscale or binary image was used imopen function;
- the removal of all connected components that have less than a given number of pixels were
performed using bwareaopen function;
- the bwperim function returns a binary image containing only the perimeter pixels of objects in
the input image;
- the imextendedmax tool identify all regional maxima that are less than a specified threshold.
Regional maxima are connected components of pixels with the same intensity value, whose
external boundary pixels all have a smaller value;
- to perform morphological closing on the grayscale or binary image we used imclose function;
- and the suppression of structures that are lighter than their surroundings and that are connected
to the image border was performed by imclearborder function [39].
3.2.2.2. Watershed
The term watershed refers to a ridge that divides areas drained by different river systems. A
catchment basin is the geographical area draining into a river or reservoir.
To explain Watershed Transform it is commonly used a comparison of a grayscale image with
a topological surface, if we imagine rain falling on this surface, the water would collect in the different
areas labelled as catchment basins. Rain falling precisely on the watershed ridge line would be
equally likely to collect in either of the two catchment basins. The watershed transform determines the
catchment basins and ridge lines in a gray-scale image [39].
The bwlabel function returns a matrix containing positive integers corresponding to the
locations of each catchment basin. [39].
3.2.2.3. Segmentation
Image segmentation subdivides an image into its constituent regions or objects. The level to
which the subdivision is carried depends on the problem being solved. That is, segmentation should
stop when the objects of interest have been isolated. For example, in the automated inspection of
electronic assemblies, interest lies in analyzing images of the products with the objective of
19
determining the presence or absence of specific anomalies, such as missing components or broken
connection paths. There is no reason to carry segmentation past the level of detail required to identify
those elements.
Segmentation of nontrivial images is one of the most difficult tasks in image processing.
Segmentation accuracy determines the eventual success or failure of computerized analysis
procedures. For this reason, considerable care should be taken to improve the probability of rugged
segmentation. In some situations, such as industrial inspection applications, at least some measure of
control over the environment is possible at times. In others, as in remote sensing, user control over
image acquisition is limited principally to the choice of imaging sensors.
Segmentation algorithms for monochrome images generally are based on one of two basic
properties of image intensity values: discontinuity and similarity. In the first category, the approach is
to partition an image based on abrupt changes in intensity, such as edges. The principal approaches
in the second category are based on partitioning an image into regions that are similar according to a
set of predefined criteria [43].
3.2.3. Features
Once nuclei are accurately segmented we extracted the morphologic and texture features of
all images used. For this purpose we used “Region Properties” tool that measures a set of properties
for each object of interest, in this case each nuclei. Toolbox for Region Properties contains two distinct
groups of functions [39] described below.
3.2.3.1. Shape Measurements
Shape measurements are a set of functions that belong to “Region Properties” tool box,
allowing the acquisition of several morphological features:
-
Area that reflects the actual number of pixels in the region.
-
Centroid that specifies the center of mass of the region, should be noted that the first element
of Centroids the horizontal coordinate of the center of mass, and the second element is the
vertical coordinate;
-
Eccentricity that specifies the eccentricity of the ellipse that has the same second-moments as
the region; Perimeter reflecting the distance around the boundary of the region [39].
3.2.3.2. Pixel Value Measurements
Pixel Value Measurements are the other set of function belonging to Region Properties tool
box, these functions allow the acquisition of information related to Pixel intensity. The features used in
this study are the MaxIntensity specifying the value of the pixel with the greatest intensity in the region;
the MinIntensity specifying the value of the pixel with the lowest intensity in the region; the
MeanIntensity specifying the mean of all the intensity values in the region [39].
This toolbox allowed to extract data of each image. Then obtained data were grouped by
mutation type.
20
3.2.4. Machine learning technique
The Unsupervised learning is a type of machine learning algorithm used to draw
inferences from datasets consisting of input data without labelled responses [39].
The most common unsupervised learning method is cluster analysis, which is used for
exploratory data analysis to find hidden patterns or grouping in data. The clusters are modelled using
a measure of similarity which is defined upon metrics such as Euclidean or probabilistic distance [39].
Unsupervised learning methods are used in bioinformatics for sequence analysis and genetic
clustering, in data mining for sequence and pattern mining, in medical imaging for image segmentation
and in computer vision for object recognition [39].
3.2.4.1. K-means
The k-means clustering is a partitioning method. The function kmeans partitions data into k
mutually exclusive clusters, and returns the index of the cluster to which it has assigned each
observation. Unlike hierarchical clustering, k-means clustering operates on actual observations (rather
than the larger set of dissimilarity measures), and creates a single level of clusters. The distinctions
mean that k-means clustering is often more suitable than hierarchical clustering for large amounts of
data [39].
The k-means method treats each observation in data as an object having a location in space.
It finds a partition in which objects within each cluster are as close to each other as possible, and as
far from objects in other clusters as possible. It is possible to choose from five different distance
measures, depending on the kind of data you are clustering [39].
Each cluster in the partition is defined by its member objects and by its centroid, or center. The
centroid for each cluster is the point to which the sum of distances from all objects in that cluster is
minimized. k-means computes cluster centroids differently for each distance measure, to minimize the
sum with respect to the measure that you specify [39].
The k-means function uses an iterative algorithm that minimizes the sum of distances from
each object to its cluster centroid, over all clusters. This algorithm moves objects between clusters
until the sum cannot be decreased further. The result is a set of clusters that are as compact and wellseparated as possible. It is possible to control the details of the minimization using several optional
input parameters to k-means, including ones for the initial values of the cluster centroids, and for the
maximum number of iteration [39].
3.2.4.2. Measurement of Internuclear Distance
Since we already had the information of Centroid feature was possible to calculate the
distance between two distinct points, in this case, two centroid of neighbouring cell in same phase.
This measurement was performed by using imtool function that displays the grayscale image in the
Image Viewer, and allows to the user joining pairs of centroids of interest [39].
The results of measurement of Internuclear Distance allow the creation of variables with
Internuclear Distance to each mutation in each phase. It was also possible to calculate the variance of
Internuclear Distance for each group.
21
3.2.5. Statistical Tests
3.2.5.1. Chi-square variance test
The Chi-square variance test is a nonparametric statistical method used to determine if two
populations have different variances In this test two hypothesis are formulated, null hypothesis
representing equal means between the two groups, and alternative hypothesis indicating a difference
between the means of both groups [44]. The result h is 1 if the test rejects the null hypothesis at the
5% significance level, and 0 otherwise [39].
3.2.5.2. Two-sample t-test for equal means and equal but unknown variances
Two-sample t-test for equal means and equal but unknown variances is used to test the
significant differences between means of two sets of data. In this test two hypothesis are formulated,
null hypothesis representing equal means between the two groups, and alternative hypothesis
indicating a difference between the means of both groups [44]. In software Matlab, the result h is 1 if
the test rejects the null hypothesis at the 5% significance level, and 0 otherwise [39].
A representative scheme with each step applied of the bioimaging pipeline established is described in
Table 2.
Image
Enhancement
Denoising
Processing
Features
Image Treshold
Shape
Measurements
Watershed
Pixel Value
Measurements
Machine learning
technique
K-means
Data Analysis
Chi-square
variance test
Two-sample
t-test
Segmentation
Table 2 – Bioimaging pipeline applied in the analysis of fluorescence images used in this study.
22
4
Results
23
4. Results
In this chapter, the results extracted using the software Matlab will be presented. The results
are intending the characterization of Total Intensity, Internuclear Distance between neighbour nucleus,
Nuclear Area and Cell Cycle Dynamic of the different populations in study, Wild-type, T340A, A634V,
R749W and E757K.
4.1. Total Intensity
Initially was extracted information related to Total Intensity which corresponds to DNA amount
stained DAPI. The Total Intensity represents the sum of each pixel Intensity of nuclei. In Figure 8 are
represented Total Intensity histograms of immunofluorescence images of cells expressing Wild-type or
mutant E-cadherin. The histograms show that all populations follow the same distribution referred on
literature, there are two peaks, the first correspond to G 1-phase and the second to G2-phase, which
has twice the amount of DNA of G1-phase.
Wild-type
60
50
40
30
20
10
0
0.5
1
a)
40
30
20
10
1
30
20
10
1.5
2
2.5
0
0.5
1
x 10
1.5
Total Intensity
d)
2
2.5
6
x 10
50
40
30
20
10
0.5
1
2.5
6
x 10
E757K
70
0
2
c)
60
0
1.5
Total Intensity
6
Number of cells
Number of cells
Number of cells
50
0.5
40
R749W
70
0
50
b)
A634V
60
0
60
0
0
Total Intensity
70
T340A
70
Number of cells
Number of cells
70
1.5
Total Intensity
e)
2
2.5
6
x 10
60
50
40
30
20
10
0
0
0.5
1
1.5
Total Intensity
2
2.5
6
x 10
f)
Figure 8 - a) DNA content profile of a cell. Histograms of Total Intensity of b) Wild-type c) T340A mutated
d) A634V mutated e) R749W mutated and f) E757K mutated populations
24
To determine if the difference of Total Intensity between the populations is statistically
significant, we applied the t-test, between Wild-type and each of mutated populations. We observed
that the difference, in Total Intensity, between Wild-type and T340A population has in fact statistical
significance. The other populations do not show significant statistical difference when compared with
Wild-type. In Table 3 is described the results from the statistical test applied to all populations.
t-test for Total Intensity
Population
Extracellular domain
Intracellular domain
Result
t-test
WT vs. T340A
≠
p<0.05
WT vs. A634V
=
-
WT vs. E757K
=
-
WT vs. R749W
=
-
Table 3 - T-Test for Total Intensity. Statistical significance was consider for p<0.05.
≠- There is statistical significant difference. = - There is no statistical significant difference.
Table 4 shows the Mean of Total Intensity for each population studied, which was obtained by
calculating the mean of Total Intensity of all nuclei of each image analyzed. Analyzing Table 4 it can
be seen that there was an increase of total intensity in T340A when compared with the ”Mean total
intensity” of the WT population.
4
Mean of Total Intensity x10
Population
Extracellular
Intracellular
Results
WT
53.519
-
T340A
61.202
Increase
A634V
57.687
-
R749W
49.870
-
E757K
55.139
-
Table 4 - Mean of Total Intensity of each population.
25
4.2. Internuclear distance
The Internuclear Distance between nuclei data was subsequently extracted as described in
the 3.2.4.2. Materials and Methods section. Table 5 represents the Internuclear Distance between
nuclei of adherent cells. The distance measurement of nuclei in this table did not take in account the
cell cycle phase of the cell. This table show that exist statistical significant difference between Wildtype and T340A or A634V populations.
t-test for Internuclear Distance
Feature
Populations
Result
t-test
WT vs. T340A
≠
p<0.05
Internuclear
WT vs. A634V
≠
p<0.05
Distance
WT vs. R749W
=
-
WT vs. E757K
=
-
Table 5 - t-test for Internuclear Distance between Wild-type and each population. p<0.05.
≠- There is statistical significant difference. = - There is no statistical significant difference.
Table 6 shows the Mean of Internuclear distance obtained by the calcule of mean of
Internuclear distance of all nulei of each population. The T340A mutated population Internuclear
distance has decreased and A634V has increased.
Mean of Internuclear distance
Population
Extracellular domain
Intracellular domain
Results
WT
60.5718
-
T340A
48.0086
Decrease
A634V
71.8435
Increase
R749W
66.0394
-
E757K
56.0178
-
Table 6 - Mean of internuclear distance of each population.
26
Table 7 represents Internuclear Distance Variance between Wild-type and each population. In
this case there is significant statistical difference between Wild-type and each of the mutated
populations.
Chi-square test of Internuclear Distance Variance
Feature
Distance Variance
Populations
Result
t-test
WT vs. T340A
≠
p<0.05
WT vs. A634V
≠
p<0.05
WT vs. R749W
≠
p<0.05
WT vs. E757K
≠
p<0.05
Table 7 - Chi-square test Internuclear Distance Variance between Wild-type and each population. p<0.05.
≠ - there is statistical significant difference, = - there is no statistical significant difference.
In Table 8 it is possible to find the Variance of Internuclear Distance values that allow verifying
that T340A, A634V and E757K variance has decrease when comparing with the Wild-type, whereas it
increased in R749W population.
Variance of Internuclear distance
Population
Extracellular domain
Intracellular domain
Results
WT
458.9624
-
T340A
141.3866
Decrease
A634V
364.0766
Decrease
R749W
465.7740
Increase
E757K
314.2888
Decrease
Table 8 - Variance of internuclear distance of each population.
27
Each population was subdivided in four clusters, corresponding to cell cycle phases and the
last one to anomalously sized and DNA-rich cells. This process was performed using k-means method
(Figure 9).
Wild-type 0039 Population
T340A mutated Population
Figure 9 - Correspondence of nuclei with cell cycle phase . Left- Wild-type 0039 Population. Right- T340A
mutated Population. Red Cluster - G1; Green cluster – S; Blue cluster - G2; Black cluster - Anomalously
sized and DNA-rich.
The image with correspondence of nuclei with the cell cycle phase of others populations are
present in Appendix B.
In order to determine if the cell cycle phase influences the Internuclear Distances between
neighbours nuclei, the distances between neighbour nuclei of same cell cycle phase were measure
(Figure 10).
Wild-type 0039 Population
T340A mutated Population
Figure 10 - Internuclear Distances between nuclei with same cell-phase
Left figure - Wild-type 0039 Population. Right figure - T340A mutated Population.
The images of Internuclear Distances between nuclei with same cell-phase of all populations
are present in Appendix C.
28
For this statistical analysis only the interphasic cell cycle phases (G1, S and G2/M) were used.
In table 9 are described the results obtained. The A634V and R749W in G1-phase and T340A in G2phase show significant statistical difference with Wild-type.
Internuclear Distance
Phases
Domain
Extracellular
G1
Intracellular
Extracellular
S
Intracellular
Extracellular
G2
Intracellular
Populations
Results
t-test
WT vs.T340A
=
-
WT vs. A634V
≠
p<0.05
WT vs. R749W
≠
p<0.05
WT vs. E757K
=
-
WT vs.T340A
=
-
WT vs. A634V
=
-
WT vs. R749W
=
-
WT vs. E757K
=
-
WT vs.T340A
≠
p<0.05
WT vs. A634V
=
-
WT vs. R749W
x
x
WT vs. E757K
=
-
Table 9 - T-test to Internuclear Distance to each sub-population. p<0.05
≠- There is statistical significant difference. = - There is no statistical significant difference. x- Variable
does not have enough samples.
29
Table 10 shows that A634V and R749W in G1-phase have an increase in “Mean of Internuclear
Distance”. T340A showed a decrease in the “Mean Internuclear” distance between nuclei in G2-phase
when comparing with the wild-type.
Phase
G1
S
G2
Population
Mean of Internuclear Distance
WT
49.4810
-
T340A
47.6756
-
A634V
72.8558
Increase
R749W
61.1984
Increase
E757K
48.5100
-
WT
65.5542
-
T340A
57.6150
-
A634V
73.2850
-
R749W
79.4867
-
E757K
64.1829
-
WT
72.9489
-
T340A
43.3800
Decrease
A634V
70.9594
-
R749W
X
-
E757K
60.1840
-
Table 10 - Mean of Internuclear Distance of each sub-population.
4.3. Nuclear area
Regarding the analysis of Nuclear area were also used statistical tests.
Table 11 shows the results of t-test for Areas and it is possible verifying that T340A, R749W
and E757K populations have significant statistical difference with Wild-type.
Nuclear Area
Feature
Nuclear Area
Populations
Nuclear Area
t-test
WT vs. T340A
≠
p<0.05
WT vs. A634V
=
-
WT vs. R749W
≠
p<0.05
WT vs. E757K
≠
p<0.05
Table 11 - t-test to nuclear area. p<0.05.
≠- There is statistical significant difference. = - There is no statistical significant difference. x- Variable
does not have enough samples.
30
Table 12 represents the Mean of Nuclear area obtained by the calcule of nuclear area mean of
each cell of each mutation. The existent differences represent a decrease of nuclear area.
Mean of nuclear area
Population
Extracellular domain
Intracellular domain
Results
WT
5327.4
-
T340A
4785.4
Decrease
A634V
5075.1
-
R749W
4580.4
Decrease
E757K
4450.5
Decrease
Table 12 - Mean of Nuclear Area of each population.
Table 13 is related with Variance of Nuclear area. It intends to understand if each group of
cells have an increase or decrease of Variance of nuclear area. Analysing the Table 13, it is possible
to detect that all mutated populations have statistical significant difference when comparing with wildtype population.
Variance of Nuclear Area
Feature
Nuclear Area
Populations
Nuclear Area
t-test
WT vs. T340A
≠
p<0.05
WT vs. A634V
≠
p<0.05
WT vs. R749W
≠
p<0.05
WT vs. E757K
≠
p<0.05
Table 13 - Chi-square test to Variance of nuclear area. p<0.05.
≠- There is statistical significant difference. = - There is no statistical significant difference. x- Variable
does not have enough samples
The table14 shows the values of Variance of Nuclear Area of each population, and it is possible to
visualize that the differences above mentioned correspond to a decrease.
Variance of Nuclear Area x10
Population
Results
WT
3.8563
-
T340A
2.0004
Decrease
A634V
3.7900
Decrease
R749W
1.6990
Decrease
E757K
3.7732
Decrease
Extracellular domain
Intracellular domain
6
Table 14 - Variance of Nuclear Area of each population.
31
Nuclear area data were also grouped in sub-populations corresponding to interphase.
Table 15 presents the results of t-test to Area. It is possible to see that A634V, R749W and
E757K in G1, all sub-populations in S and E757K in G2 have significant statistical difference.
Nuclear Area
Phases
Domain
Extracellular
G1
Intracellular
Extracellular
S
Intracellular
Extracellular
G2
Intracellular
Populations
Results
t-test
WT vs.T340A
=
-
WT vs. A634V
≠
p<0.05
WT vs. R749W
≠
p<0.05
WT vs. E757K
≠
p<0.05
WT vs.T340A
≠
p<0.05
WT vs. A634V
≠
p<0.05
WT vs. R749W
≠
p<0.05
WT vs. E757K
≠
p<0.05
WT vs.T340A
=
-
WT vs. A634V
=
-
WT vs. R749W
x
x
WT vs. E757K
≠
p<0.05
Table 15 - T-Test to nuclear area of each subpopulation. p<0.05.
≠- There is statistical significant difference. = - There is no statistical significant difference. x- not enough
samples were available to perform the statistical tests.
32
In Table 16 it is possible to verify that the detected differences correspond to a decrease. In this
table it is also possible to verify that the nuclear area of Wild type population increase along the cellcycle.
Phase
G1
S
G2
Population
Mean of Nuclear Area
WT
4053.9
-
T340A
3966.8
-
A634V
3597.7
Decrease
R749W
3745.9
Decrease
E757K
3463.2
Decrease
WT
5830.8
-
T340A
5044.7
Decrease
A634V
5022.6
Decrease
R749W
4672.1
Decrease
E757K
5147.9
Decrease
WT
6464.6
-
T340A
5899.1
-
A634V
6133.4
-
R749W
x
x
E757K
5469.7
Decrease
Table 16 - Mean of Nuclear Area of each sub-population. p<0.05.
. x- not enough samples were available to perform the statistical tests.
In order to analyze influence of E-cadherin mutations in dynamic of cell cycle, statistical tests
were also applied. Figure 11 displays the plots containing the clusters correspondents to cell-cyclephases. The red Cluster correspond to G1-rich sub-population, green cluster to S, blue cluster to G2
and black cluster to anomalously sized and DNA-rich cells (aneuploid). The plots show that there is an
increase of Total intensity and Nuclear area as the cell progress into cell-cycle.
33
Wild-Type
4
x 10
1.5
1
0.5
0
T340A
4
2
Area x104
Area x104
2
x 10
1.5
1
0.5
0
0.5
1
1.5
2
Total Intensity
0
2.5
0
0.5
6
1
1.5
2
2.5
Total Intensity
x 10
6
x 10
Figure 11 - Plots Total Intensity vs. Nuclear Area. Red Cluster - G1, Green cluster - S, Blue cluster - G2,
Black cluster - Anomalously sized and DNA-rich cells
With data grouped into clusters it is possible to extract the percentage of nuclei in each cell
cycle phase.
The percentages of nuclei in each phase are presented in Table 15.
Percentage of nuclei in each phase
Population
G1
S
G2
Other
WT
47.84%
23.92%
25.25%
2.99%
T340A
56.72%
17.91%
22.39%
2.99%
A634V
47.12%
9.62%
37.50%
5.77%
R749W
54.26%
18.09%
26.6%
1.06%
E757K
59.13%
24.35%
12.17%
4.35%
Table 17 - Percentage of nuclei in each phase. “Other” represents the aneuploid cells identified in the
immunofluorescence images.
In Table 16 are presented statistical results to percentages nuclei in each phase. It is possible
to verify that all mutated populations shown no significant statistical difference with the Wild-type when
concerning the cell cycle dynamic.
t-test to percentage nuclei in each phase
Population
Extracellular domain
Intracellular domain
Result
WT vs. T340A
=
WT vs. A634V
=
WT vs. E757K
=
WT vs. R749W
=
Table 18 - t-test to percentage nuclei in each phase. p=0.05
≠- There is statistical significant difference. = - There is no statistical significant difference. x- Variable
does not have enough samples
34
5
Discussion
35
5. Discussion
In this chapter, the results previously presented will be discussed. The results related with
Total Intensity, Internuclear distance, Nuclear area and Cell-cycle will be interpreted in a biological
context.
Total Intensity
Regarding Total Intensity, the analysis of DAPI staining shows that the algorithm used in this
study is reliable, once the histograms, shown in Figure 8 of section 4.1 are consistent with the
literature. In the figure there are two peaks in each histogram, the first correspondent to G 1-phase
(diploid cell) and the second to G2-phase (tetraploid cell), which has twice the amount of DNA of G1phase.
The statistical analysis performed with Total Intensity results showed that there is no
significant statistically difference between Wild-type and the populations expressing A634V, R749W
and E757K mutations. Only T340A population showed a statistically significant in “Total Intensity”
when comparing with Wild-type population. Based in our results, the expression of T340A mutant Ecadherin leads to an increase in DNA content of cells. This may be explained by the destabilization
created by the loss of function of E-cadherin. This phenomenon needs to be further studied.
Internuclear Distance
As previously mentioned, the main function of E-cadherin is cell-cell adhesion. A mutation in
this molecule may cause a disturbance in cellular adhesion and organization. Therefore it may cause
an increase of Intranuclear distance, justifiable by the loss of binding between cells. Nevertheless, in
the presence of decrease of nuclei or cell, the Intranuclear distance may decrease.
Concerning the results shown in the previous chapter related with Intranuclear distances
between neighbouring cells, it is possible to detect differences between Wild-type and T340A or
A634V mutated populations. T340A Internuclear distance has decreased and A634V has increased. It
would be expected E-cadherin mutation provoked an increased distance between nuclei, as observed
for the A634V mutation, once that mutated E-cadherin does not establish a strong enough contact with
catenins, and proteins from the endocytic machinery have the opportunity to bind, initiating the
endocytosis process that can culminate in E-cadherin degradation. The morphological alterations
observed in cells expressing the T340A mutation may be explained by the fact that this phenotype
may be producing more compact cell clusters or by a decreased of the nuclear area, which may be
associated to the fact that T340A mutation is only slightly affected the interplay with p120-catenin,
which emphasizing the importance of these partners for E-cadherin-mediated cell–cell adhesion [14].
On Internuclear Distance Variance feature is intended to evaluate the pattern of Internuclear
Distance, there is, if cells tend to remain adherent or not. Our results showed that T340A, A634V and
E757K presented lower Internuclear Distance Variance than wild-type population. These results
suggest that most cells tend to have the same behaviour: the “Internuclear Distance Variance” of
36
T340A population decreases, in A634V-expressing cells it
increases and in cells expressing the
E757K E-cadherin mutation maintain the variance of their Internuclear Distances. Cells from R749W
population have an higher Internuclear Distance Variance, which means that the expression of this Ecadherin mutation leads to nuclei with significantly different sizes.
When populations were grouped into cell cycle phase clusters, we could observed that cells
expressing A634V or R749W mutations and in G1-phase have an increased Internuclear Distance.
Moreover, cells expressing T340A also showed a statistically significant difference with the wild-type
population but only in G2-phase and with a decreasing trend. It seems that these cell cycle phases
have a higher weight in each population justifying the statistical differences. In T340A mutated
population the fact of cells are closer or smaller apparently is due to cells in the G2-phase. In A634V
and R749W the cells are further apart due to the contribution of G1-phase. Despite the statistical
significant difference observed in G1-phase for R749W population it seems that the contribution of
those G1-phase cells does not seem to affect the population as a whole.
Nuclear Area
Relatively to Nuclear Area the results showed a decrease in this feature for cell expressing the
T340A mutation, which is in agreement with the decrease of Internuclear distance mentioned above.
The size of nucleus decreases, so the distance between centroids (the Internuclear distance),
decreases also.
The R749W and E757K populations showed also a decrease in this feature. It would be
expected, that in these populations, the nuclear area would increase. Cells can lose their cellular
structure when E-Cadherin function is compromised, which may cause a flatness phenomenon
conferring an increase of nuclear area. However, this decrease could be related to disturbances in
signalling pathways. Many different signalling molecules have been reported to interact with Ecadherin, and many different signalling pathways are linked to this protein, nonetheless, their effects
seem to be dependent on the specific cell type and context [45]. These mutations seem to
compromise cell growth.
When analyzed by cell cycle phase it is possible to see that cells in S phase from all mutant
populations, or cells in G1 phase from A634V, R749W and E757K populations, and even cells in G2
phase from E757K mutant population show a decrease in nuclear area. Moreover, we could verify that
the overall decrease of nuclear area in T340A mutated population is mainly due to the S-phase
contribution. In A634V, although G1 and S phases present differences, population as a whole does
not. The R749W and E757K mutated populations results presented by group of distinct phases are
consistent with the results of all cells when taken into account all cells. It is possible to verify that G1
and S phases nuclear area are more affected by E-cadherin mutation which may be related to the fact
that the nuclear increase in wild type population G2-phase not be so marked, and this is reflected in
the result of no statistical significant differences.
37
Cell Cycle
In relation to percentage of nuclei in each phase of cell cycle, the results show that there is no
statistically significant difference between all mutated populations and wild-type population. These
results mean that E-cadherin mutations do not interfere in the cell cycle dynamic. These results are in
agreement with those obtained by flow cytometry for the Intracellular domain populations (R749W and
Mean percentage cells±SEM
E757K).
70
60
50
40
WT
30
2245
20
2269
10
0
G1
G2
S
Cell cycles phases
Figure 12 - Percentage of cell cycle phase, obtained by Flow Cytometry(IPATIMUP).
The percentage of cells in each phase demonstrate that E-cadherin mutations do not influence
the dynamic of the Cell cycle. Phenomenon like energy metabolism, biosynthesis, signal transduction,
movement, differentiation, and reproduction, required in regulation of complex network of protein
interactions do not seem to be affected by E-cadherin [46].
The cell cycle is rather regulated by a complex of protein interactions that control the activities
of cyclin-dependent kinases. The destruction of mitotic cyclin-dependent kinases activity at anaphase
allows cells to divide and enter G1 phase of the next cell cycle. Exit from mitosis is controlled by the
anaphase promoting complex, which initiates the degradation of cohesins and mitotic cyclins [46].
Therefore, the interaction of E-cadherin with others molecules involved in cell adhesion do not
affect the dynamic of cell-cycle.
38
6
Conclusion
39
6. Conclusion and Future perspectives
In this study was applied an algorithm to a set of images collected through fluorescence
microscopy. The aim of this application was to analyse the morphologic and cell cycle changes related
to distinct CDH1 missense mutations, using the selection of cells of interest, and calculating
morphological and texture parameters.
The Total Intensity feature showed that the algorithm used in this study is reliable, since the
distribution of DNA amount of all populations present the same distribution of literature. The plots
obtained display two peaks, the first correspondent to G1-phase, and the second to G2-phase, which
has twice the amount of DNA of G1-phase. The Total Intensity analysis showed also that only T340A
mutated population is statistically different from Wild-type population; apparently the presence of
T340A mutation causes an increase in cellular DNA content. This mutation seems to have a different
behaviour from the other mutations.
Intranuclear distances between neighbouring cells, it is possible to conclude that T340A
Internuclear distance has decreased and A634V has increased, as expected.
On the subject of Internuclear Distance Variance, in T340A population are cells mostly
smaller, in A634V bigger and E757K identical comparing with wild-type population. The R749W have
a higher Distance Variance, which means that there are nucleus with substantially different sizes.
When populations were grouped into clusters demonstrated that in A634V and R749W the
cells are further apart due to the contribution of G1-phase. Although the results relative to the R749W
population have a statistical significant difference in G 1-phase, the number of cells or the weight of that
cells does not seem to affect the population as a whole.
Relatively to Nuclear Area, it is possible to conclude that T340A, R749W and E757K mutated
populations have a decrease of nucleus size, the size of nucleus decrease so the distance between
centroids, Internuclear distance, become smaller. In A634V, although G1 and S phases present
differences, population as a whole does not. The R749W and E757K mutated populations results
presented by group of distinct phases are consistent with the results of all cells when taken into
account all cells.
The percentage of nuclei in each phase of cell cycle, allows to conclude that there is no
significant statistically difference between all mutated populations and wild-type population. These
results mean that E-cadherin mutations do not interfere with the cell cycle dynamics. These results
coincide with those obtained by flow cytometry, studying Intracellular Mutations.
It is possible to conclude that the mutation of E-cadherin does not affect the dynamic of cellcycle.
The algorithm described and explored herein showed to be a useful tool in the study of HDGC.
Additionally it may also be applied in other aneuploid carcinomas. It is still possible to use it to aid in
the diagnosis, for instance in the National Programme for Oncologic diseases, namely of cervical
cancer, as it would aid in the assessment of morphological changes of the collected cellular samples.
40
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A
Appendix A - Image with Correspondence of nuclei with
cell-phase
45
The images present in Appendix A were used to group the cells per phase. The figures 13 to
17 are from wild-type population. The figures 18 and 19 represent cells expressing A634V; figures 20
and 21 correspond to cells expressing R749W and finally figures 22 and 23 show cells expressing
E757K. The red dots represent the cells rich in G1-phase, green dots in S-phase, blue dots in G2,
Black cluster the aneuploid cells
Figure 13 - Correspondence of nuclei with cell-phase of Wild-type 0003 population
Figure 14 - Correspondence of nuclei with cell-phase of Wild-type 0009 population
46
Figure 15 - Correspondence of nuclei with cell-phase of Wild-type 0011 population
Figure 16 - Correspondence of nuclei with cell-phase of Wild-type 0037 population
Figure 15 - Correspondence of nuclei with cell-phase of Wild-type 0038 population
47
Figure 18 - Correspondence of nuclei with cell-phase of A634V 1901-0045 mutated population
Figure 19 - Correspondence of nuclei with cell-phase of A634V 1901-0048 mutated population
Figure 20 - Correspondence of nuclei with cell-phase of R749W 2245-0049 mutated population
48
Figure 21 - Correspondence of nuclei with cell-phase of R749W 2245-0050 mutated population
Figure 22 - Correspondence of nuclei with cell-phase of E757K 2269-0057 mutated population
Figure 23.- Correspondence of nuclei with cell-phase of E757K 2269-0059 mutated population
49
50
B
Appendix B - Image with Correspondence of nuclei with
cell-phase
51
In Appendix B are depicted representative figures that demonstrate how selection of neighbour cells
was made for the measurement of Internuclear Distance.
Figure 24 - Image with Correspondence of nuclei with cell-phase of Wild-type 0003 population
Figure 25 - Image with Correspondence of nuclei with cell-phase of Wild-type 0009 population
Figure 26 - Image with Correspondence of nuclei with cell-phase of Wild-type 0011 population
52
Figure 27 - Image with Correspondence of nuclei with cell-phase of Wild-type 0037 population
Figure 28 - Image with Correspondence of nuclei with cell-phase of Wild-type 0038 population
Figure 29 - Image with Correspondence of nuclei with cell-phase of Wild-type 0038 population
Figure 30 - Image with Correspondence of nuclei with cell-phase of mutated 1018-0041 population
53
Figure 31 - Image with Correspondence of nuclei with cell-phase of mutated 1091-0045 population
Figure 32 - Image with Correspondence of nuclei with cell-phase of mutated 1091-0048 population
Figure 3316 - Image with Correspondence of nuclei with cell-phase of mutated 2245-0048 population
54
Figure 34 - Image with Correspondence of nuclei with cell-phase of mutated 2245-0050 population
Figure 3517 - Image with Correspondence of nuclei with cell-phase of mutated 2269-0057 population
Figure 36 - Image with Correspondence of nuclei with cell-phase of mutated 2245-0050 population
55
C
Appendix C - Plots of Total Intensity vs Area
56
The figures in Appendix C represent the clusters of cells in each phase of cell-cycle. The red dots
represent the cells rich in G1-phase, green dots in S-phase, blue dots in G2, Black cluster the
aneuploid cells.
A634V
4
Area
x10
4
2
x 10
1.5
1
0.5
0
0
0.5
1
1.5
2
Total Intensity
2.5
6
x 10
Figure 18 - Plots Total Intensity vs. Area of A634V population
R749W
4
x 10
x10
1.5
Area
4
2
1
0.5
0
0
0.5
1
1.5
2
Total Intensity
2.5
6
x 10
Figure 38 - Plots Total Intensity vs. Area of R749W population
57
E757K
4
Area
x10
4
2
x 10
1.5
1
0.5
0
0
0.5
1
1.5
2
Total Intensity
2.5
6
x 10
Figure 19 - Plots Total Intensity vs. Area of E757K population
58