University of Groningen Evolution of karyotype landscapes in cancer Bakker, Bjorn

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University of Groningen

Evolution of karyotype landscapes in cancer

Bakker, Bjorn

DOI:

10.33612/diss.166886747

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36 | Chapter 2

ABSTRACT

Aneuploidy, an aberrant number of chromosomes in cells, is a feature of several syndromes associated with cognitive and developmental defects, a hallmark of cancer cells and has been suggested to play a role in neurodegenerative disease. To better understand the relationship between aneuploidy and disease, various karyotyping methods have been developed, each with their own advantages and limitations. While some methods rely on dividing cells and thus bias aneuploidy rates to that population, other, more unbiased methods can only detect the average aneuploidy rates in a cell population, cloaking cell-to-cell heterogeneity. Furthermore, some techniques are more prone to technical artefacts, which can result in over- or underestimation of aneuploidy rates. In this review, we provide an overview of most ‘traditional’ karyotyping methods as well as the latest high throughput next generation sequencing karyotyping protocols with their respective advantages and disadvantages.

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How to count chromosomes in a cell: an overview of current and novel technologies | 37

INTRODUCTION

A brief history of aneuploidy

During each cell division all chromosomes are duplicated and distributed equally over the two emerging daughter cells. Various checkpoints help to ensure that chromosome segregation occurs accurately during the cell cycle. When errors occur during mitosis, cells can end up with an abnormal number of chromosomes, a state called aneuploidy. The relation between aneuploidy and disease was first hypothesized early in the 20th century by Theodor Boveri. By injecting multiple sperm cells rather than one into sea urchin embryos, he showed that an increased chromosome content can result in abnormal

development or death1–3_{. From the mid-20}th_{century on, a large number of assays have}

been developed to quantify aneuploidy, which allowed linking various human syndromes and diseases to aneuploidy.

Most systemic aneuploidies for human autosomes are incompatible with embryonic development, and those that are viable (trisomies for chromosomes 13, 18 and 21) all result in severe developmental and cognitive defects. The most well-known aneuploidy-related syndrome is Down’s syndrome, which was first linked to a trisomy for chromosome 21 in

19594_{. One year later, Edward’s syndrome (trisomy 18) and Patau’s syndrome (trisomy 13)}

were uncovered as congenital syndromes caused by chromosomal copy number changes5,6_.

Collectively, these syndromes demonstrate the severe consequences of an additional chromosome at the organismal level.

The role of aneuploidy in cancer

Cancer cells frequently exhibit errors in chromosome segregation, resulting in chromosomal

imbalances7_{. In fact, roughly two out of three human tumours display aneuploidy}8,9_{, and}

genomic instability is considered to be a major enabling characteristic of malignant

transformation10_{. Paradoxically, studies in aneuploid yeast strains and mouse embryonic}

fibroblasts have shown that aneuploidy reduces cell fitness and leads to growth defects,

metabolic and proteotoxic stresses11–13_{. It is therefore remarkable that aneuploid cancer}

cells can proliferate in vivo despite aneuploidy-induced stress14_{and suggests that aneuploid}

cancer cells somehow adjust their physiology to cope with the detrimental consequences of aneuploidy.

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38 | Chapter 2

Another disease that links cancer to aneuploidy is the mosaic variegated aneuploidy syndrome (MVA). MVA is caused by mutations in the spindle assembly checkpoint (SAC) gene BUB1B. The SAC monitors kinetochore-microtubule attachment during metaphase, and blocks anaphase onset until all chromosomes are properly aligned and attached to microtubules, thus preventing chromosome missegregation and any resulting

aneuploidy15_{. MVA patients are indeed characterized by random aneuploidies and suffer}

from developmental and cognitive defects16,17_{. Furthermore, they are significantly more}

likely to develop paediatric cancers, including rhabdomyosarcoma, Wilms tumour and

leukaemia16_{, emphasizing the link between aneuploidy and cancer.}

Finally, mouse models have been instrumental to better understand the link between aneuploidy and cancer. In these models, chromosomal instability (CIN) was provoked in vivo by inactivation of SAC genes, reviewed extensively elsewhere18–20_{. Briefly, CIN} results in four major phenotypes: 1) embryonic lethality when the SAC is fully alleviated in the whole organism; 2) weak tumour predisposition when SAC genes are heterozygously inactivated; 3) tumour suppression in some tumour predisposed backgrounds; and 4)

premature ageing18–20_{. These phenotypes even differ between in vivo cell lineages: for}

instance, the basal layer in mouse epidermis copes much better with CIN than the hair

follicle stem cells that reside in the same tissue21_.

All together, these data indicate that while CIN has an important role in tumorigenesis CIN alone might not be sufficient to initiate malignant transformation, but that further predisposing mutations are required to convert aneuploid cells into aneuploid cancer cells. The role of aneuploidy in neurodegeneration

However, various studies have suggested that aneuploidy is not unique to cancer cells. For

instance, a large fraction of normal mouse22_{and human}23–25_{neurons appear to be aneuploid.}

Strikingly, these aneuploid neurons seem to be fully functional as they are integrated into

the brain circuitry and can be activated26_{. While the levels of aneuploidy in healthy brain}

are still under debate27,28_{, aneuploidy in the brain could play a role in neurodegeneration}29_.

For instance, patients suffering from Alzheimer’s Disease (AD) exhibit frequent copy number changes for chromosomes 17 and 21 in buccal cells. Furthermore, post-mortem

AD brain tissue appears to be more aneuploid than age-matched control brain28,30_{. Possibly,}

aneuploidy contributes to neurodegenerative diseases through proteotoxic stress leading to misfolded proteins, protein aggregates and thus neurodegeneration. However, most of these studies relied on noisy techniques to quantify aneuploidy in post-mitotic cells (e.g. in

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situ interphase FISH), which might have resulted in over- or underestimation of the actual aneuploidy in neurons. Therefore, improved karyotyping platforms for non-dividing cells will be required to further substantiate the role of aneuploidy in neurodegenerative disease. Accurate tools for karyotyping

To understand the role of aneuploidy in pathologies like cancer and neurodegenerative disease, accurate karyotyping is of the utmost importance. While various karyotyping methods have been developed since the late 1950s, their accuracy and reliability differ. Importantly, each karyotyping method is limited as to which cytogenetic abnormalities can be detected, and in which cell type (e.g. proliferating vs. post-mitotic). Furthermore, each method is prone to its own technical and biological artefacts that need to be considered. In this review we will provide an overview of the most common techniques, along with a number of new cytogenetic methods, with their applications and limitations.

Cytogenetic methods

A first important consideration when selecting a protocol for karyotyping is the type of cells to be assessed: dividing cells or non-dividing cells. While essentially all cytogenetic methods can be used to quantify chromosome copy numbers, some are either restricted to only a few chromosomes, reducing the resolution of the analysis, or cannot detect some types of karyotypic abnormalities (e.g. balanced translocations), which we will further discuss below. The detection limits of all of the addressed methods are summarized in Figure 1.

Metaphase-spread based karyotyping methods

Traditional karyotyping and fluorescent in situ hybridization (FISH)

Traditional metaphase spread-based karyotyping requires cycling cells. For this, cells are arrested in metaphase to simplify chromosome counting using spindle assembly checkpoint poisons such as colcemid. Cells are then incubated in a hypotonic solution followed by fixation. The fixed cells are then spotted on a microscope slide to scatter the chromosomes and stained with Giemsa or DAPI to visualize the chromosomes. This protocol allows for the detection of whole chromosome copy number gains and losses, as well as large amplifications and deletions (by assessing Giemsa chromosome banding patterns). Metaphase karyotyping can be combined with fluorescence in situ hybridization (FISH), for instance to detect common copy number variations (CNVs) or translocations such as BCR-ABL t(9;22)(q34;q11) translocation. FISH is a powerful tool to establish cytogenetic abnormalities in patients and in pre-implantation embryos in the clinic. Unfortunately,

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40 | Chapter 2

FISH can only detect a small number of features per cell, as a result of limitations in the number of fluorescent labels. Furthermore, technical artefacts such as probe clustering or failure of hybridization, or incomplete spreads can result in an over- or underestimation of the targeted feature or chromosome. Finally, FISH requires technical expertise and quantification is labour intensive for which automation is complex. This makes FISH a powerful tool to detect recurrent chromosomal abnormalities in a standardized setting, but

less suitable for the detection of random aneuploidies31–33_.

Spectral karyotyping

Spectral karyotyping (SKY), a FISH-adapted protocol, can be used to detect both chromosome copy number changes as well as gross translocations in the entire genome. For this, metaphase chromosome spreads are prepared on a microscope slide, similar to FISH. Instead of one labelled probe per chromosome, chromosome-specific probe sets consisting of up to five distinct fluorescent dyes are hybridized to metaphase chromosomes, resulting in chromosome-specific, unique combinations. This allows for simple detection of all chromosomes in a metaphase spread, which typically is further simplified by computer post-processing of the imaging data, resulting in images in which whole chromosomes are artificially stained in unique colours. This allows for the detection of structural as well as numerical aberrations at first glance. Unlike with next-generation sequencing (see below), chromosome fragments can also be identified as individual fragments, when present. This makes SKY an ideal tool to detect gross chromosomal instability in dividing cells. The

technical limitations of SKY are similar to those of FISH32,34–38_.

Limitations to metaphase-spread based karyotyping – something’s FISHy

While metaphase-spread based karyotyping is a powerful technique to detect aneuploidy, one important limitation is the requirement of dividing cells. In some cases, dividing cells are not available, e.g. in case of paraffin-embedded material or primary tumour material. While tissue culture cells typically divide at least once per day, with about 50% of cells in S-phase at any one time, the doubling time of e.g. primary breast cancer cells

can be as low as ~1 - 10 months, with 2-5% of cells in S-phase39_{. Furthermore, when}

harvesting primary tumour material, colcemid-mediated enrichment of mitotic cells to obtain condensed chromosomes is not possible, which, together with low proliferation rates disqualifies metaphase-dependent aneuploid-quantification.

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Te chn iq ue W ho le ge no m e An eup lo id y Po ly pl oi dy CN Vs (s iz e) In ve rs io n Re cipr oc al tr an sl oc at io n Un ba la nc ed tr an sl oc at io n H et er og en eit y Co st s Re fe re nc e M et ap ha se spr ea d-ba se d Gi em sa st ai ni ng + + + 5-10 M b + + + + In ex pe ns iv e [3 6, 4 9] FI SH - + + + + + + + In ex pe ns iv e [3 3, 3 8, 4 9] SK Y + + + - - + + + M od er at e [3 2, 3 4– 37 ] N on - m et ap ha se spr ea d-ba se d Int er ph as e FI SH - + + + + + + + In ex pe ns iv e [3 3, 3 8, 4 9] CG H & a CG H + + - 10 -2 0 M b - - + (+ ) 1 M od er at e [3 3, 3 6, 4 5 44, 46, 47 ] SN P ar ray + + - 50 0 kb - - + (+ ) 1 M od er at e [3 6, 4 8, 50 – 50 ] Fl ow c yt om et ry - (+ ) 2 + - - - - - In ex pe ns iv e [4 2, 4 3] D ig it al kar yot yp in g + + - 0. 5-2 M b - - + - M od er at e [5 3– 58 ] Si ng le -c el l se qu enc in g + + (+ ) 3 20 0-50 0 kb 4 (+ ) 5 (+ ) 5 + + Exp en si ve [2 7, 6 1, 6 2] Fig. 1 Comparison of cytogenetic methods. Cytogenetic techniques to detect chromosomal abnormalities are listed, as well as their ability to detect various chromosomal aberrations. A ‘+’ indicates the technique is able to detect the abnormality , a ‘-‘ indicates an inability to detect. A (+) signifies that the method can detect the aberration only under

certain conditions or in a limited fashion, the details of which are listed below

. If a technique can detect local CNVs, whenever possible the minimum detectable CNV

size is

given in kb (kilobases) or Mb (megabases). 1.

Multiple experiments have to be performed on subpopulations of the same sample in order to identify heterogeneity

. 2. Bulk aneuploidy can be detected using flow cytometry , i.e. a deviating DNA content from the haploid genome or a multiple thereof. Heterogeneity and specific copy number

changes cannot be determined.

3.

Polyploidy using single-cell sequencing can only be detected using a non-WGA

approach in which more than two identical reads are mapped to the reference genome.

4.

The minimum detectable CNV

size is heavily influenced by the coverage.

Here we provide a conservative estimate based on 1% coverage.

5. Bo th in ve rsi on s an d re ci pr oc al tr an slo ca tio ns m ay o nl y be d et ec ta bl e wi th si ng le -c el l se qu en ci ng if su ffic ie nt c ov er ag e ov er th e br ea kp oi nt re gi on c an b e ac hi ev ed . Alternatively

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42 | Chapter 2

Furthermore, as aneuploidy has such detrimental consequences for cell fitness and

proliferation13,40_{and cancer cells appear to select for some chromosome combinations}41_{, it is}

likely that the observed aneuploidy in the mitotic cell population is not fully representative of the total (tumour) cell population. In addition, metaphase-dependent methods preclude analysis of post-mitotic cells, such as neurons or quiescent stem cells. Therefore, to reliably quantify aneuploidy, protocols are required that do not depend on mitotic chromosomes, which we will further discussed below.

Non-metaphase based karyotyping methods Interphase FISH

The classical way to quantify aneuploidy in non-dividing cells is by interphase FISH. Interphase FISH (I-FISH) is very similar to metaphase FISH and also relies on chromosome-specific probes, which are hybridized to uncondensed chromosomes in interphase instead of hybridization to metaphase chromosomes, followed by counting the foci per nucleus for each probe. Therefore, the limitations of interphase FIHS are similar to metaphase FISH: under and over-quantification of aneuploidy due to failure of probe hybridization or probe clustering, respectively. Similarly, the number of available fluorophores limits the number of quantifiable chromosomes.

An adapted version of this technique, multicolor banding (MCB), reduces underestimation or overestimation of aneuploidy caused by the above-mentioned FISH technical artefacts. In this case multiple probes per individual chromosome are hybridized to one chromosome only, resulting in a coloured banding pattern for this chromosome, thus increasing reliability. The flip side of the increased reliability is further loss of resolution as with MCB only one chromosome can be assessed per analysis.

Flow cytometry

A fairly simple but low resolution method to determine the ploidy of many cells at once is by fluorescently labelling the DNA with a dye, followed by flow cytometry. By comparing the signal of the sampled cells of unknown ploidy to diploid reference cells, one can simply extrapolate the ploidy of the sample by looking at its relative fluorescent signal. For instance, a tetraploid genome would result in twice the signal compared to a diploid cell population. Intermediate signals in the sample would imply gross aneuploidy on a population level. While large numbers of cells can be assessed at once and the preparation time is minimal, the resolution limit of this method is very minimal: individual chromosome copy number gains or

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(Array) comparative genomic hybridization

Another method to quantify aneuploidy in non-dividing cell populations is comparative genomic hybridization (CGH). For this, genomic DNA of the to be assessed sample (e.g. a tumour) is fragmented and labelled with a green fluorescent dye, and DNA from a normal diploid (isogenic) reference control is labelled in red. Both sample and reference DNA are then hybridized to a diploid metaphase spread from a cell line from the same organism. Fluorescence ratios are then determined through fluorescence microscopy. In this example, an increased green signal implies amplification of a specific region or a gain of a whole chromosome in the tested sample, and red implies a deletion. Finally, a DNA stain is used to identify the individual chromosomes. The approximate resolution of CGH

is ~10-20 Mb44_.

CGH has mostly been replaced by a more sophisticated and higher resolution adapted version employing microarrays (array CGH or aCGH). For aCGH distinctly fluorescently labelled DNA from a sample in one colour and a reference in another are hybridized competitively to a reference genome. However, instead of using a metaphase spread, an array chip containing defined genomic probes is used for hybridization. Fluorescence ratios are then determined in a microarray scanner. Depending on the probe density/sizes used, the resolution can be as high as ~400 kb, sufficient to quantify copy alterations for individual loci.

One important limitation for both CGH and aCGH is that neither method can detect reciprocal translocations or inversions since such abnormalities do not result changes in the chromosomal content. Furthermore, as typically the genomic DNA of tumour fragments (and not individual cells) is hybridized, only clonal copy number changes will

be detected33,36,44–49_.

Single nucleotide polymorphism array

Another array-based technique to detect chromosome copy numbers is the single nucleotide polymorphism array (SNP array). Instead of competitively hybridizing sample and reference genomes, only the sample labelled genome is hybridized to an array with roughly 100,000 SNP probes. Copy number changes are then determined by comparing the fluorescent signal from the labelled sample to an independently hybridized control. In addition to assessing copy number changes, SNP arrays can also be used to detect ratios between parental chromosomes. For instance, SNP arrays allowed for the detection of uniparental disomies in leukaemia 36,48,50–52_{. The limitations of SNP arrays are similar to array CGH.}

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44 | Chapter 2

Digital karyotyping

Digital karyotyping can be used to quantify aneuploidy at relatively high resolution. For digital karyotyping, the genomic DNA of the examined cells is first digested using a restriction enzyme, and resulting fragments are ligated to a biotinylated linker. The ligated fragments are captured by streptavidin beads and further restriction enzyme-digested to produce linker compatible overhangs. After ligation of the linkers, the fragments are released from the streptavidin beads by digestion with the more exotic restriction enzyme MmeI. The resulting 21-base pair fragments (tags) are then ligated together to form ‘ditags’ that now have compatible overhangs. The ditags are concatenated, PCR-amplified and cloned into a vector, followed by sequencing and alignment to the reference genome. Thus, the copy number of a particular region can be

quantified based on the density of the sequenced tags with a resolution up to a theoretical 4 kb53–

55_{. In practice, the method has allowed for identification of genomic deletions and amplifications}

in various cancers at a resolution of ~500 kb to 2 Mb 56–58_{. The actual resolution is primarily}

limited by the efficiency of the tag library preparation and therefore the number of tags that can be acquired from the sample of choice. Therefore, the procedure has to be optimized per cell line or sample which is labour and therefore cost intensive.

New tools for karyotyping

While aCGH, SNP arrays and digital karyotyping protocols allow for aneuploidy quantification at much higher resolutions within the genome, it does not allow for the quantification of karyotype heterogeneity (i.e. differences among the karyotypes within the cell population). An ideal karyotyping method would therefore combine the best of both worlds: a single cell approach with high resolution, which might come from new sequencing-based karyotyping methods.

Next-generation sequencing (NGS) technology has opened up new possibilities to explore both the human and the mouse genome, which has allowed us to map mutations in various oncogenes and tumour suppressors at the single base level. Other than determining the DNA nucleotide sequence, NGS can also be used to study tumour evolution. For example, high-throughput and high coverage sequencing allows the use of SNPs to map the clonality

of tumours59,60_{. It is also possible to karyotype cells using NGS.}

Single-cell sequencing as a novel method for karyotyping

Single-cell next generation sequencing is a recently developed platform to quantify karyotypes of single cells, and has recently been used to quantify aneuploidy levels in liver,

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single cells can be collected using cell pickers, serial dilutions, or FACS. As the input DNA from a single cell is very limited, library preparation in some protocols starts with a whole genome amplification step. The DNA is then fragmented, end-repaired, phosphorylated and A-tailed to prepare the DNA for adaptor ligation, to make the DNA fragments compatible with the sequencing platform used. Following adapter ligation, the library fragments are PCR-amplified, while adding barcode sequences that allow for multiplexing libraries in individual sequencing lanes, which is followed by next generation sequencing. Following NGS, the libraries are demultiplexed, run through a quality control pipeline and reads are

mapped to the reference genome27,61_.

Chromosome copy numbers and local CNVs per cell can then be extracted from the NGS data by examining the number of reads per chromosome or region, for instance using a

Hidden Markov model27_{. This yields data with comparable or much higher resolution than}

array CGH (resolutions up to 20-50 kb are feasible depending on coverage) and at the single cell level. High resolution sequencing data of the assessed genome is not required to determine chromosome copy numbers faithfully, for this 0.5-1% coverage per cell is more than sufficient. A lower coverage threshold also implies that hundreds of cells can be sequenced in one run, which is why multiplexing of NGS libraries is a requirement, reducing sequencing costs. Furthermore, the entire library preparation process can be automated using robotic pipetting system, reducing labour costs.

A major advantage of single cell sequencing over FISH is the ability to look at all chromosomes simultaneously in a single cell. In this way, the complete karyotype can be determined for each individual cell. Importantly, the risk of over- or underestimation of copy numbers is greatly reduced because for each chromosome thousands of reads are sequenced, instead of assessing only a few loci per chromosome. Indeed, several recent reports on copy number variation and aneuploidy in normal brain cells using single cell sequencing emphasize the advantages of using whole genome single cell sequencing over FISH27,62,63_.

Single cell NGS also comes with downsides. As mentioned above, for now NGS is still expensive, especially when compared to lower resolution karyotyping protocols. Furthermore, it is not possible to reliably detect balanced translocations and inversions, especially at lower coverage. However, if coverage is sufficient, translocations can be identified from ‘chimeric’ sequencing reads (i.e. one read aligning to two chromosomes) that bridge the translocation breakpoint. Acquiring the required coverage for a single

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46 | Chapter 2

cell to map these reads is not trivial and heavily depends on the efficiency of the library preparation and the sequencing platform parameters and used bio-informatical tools. Unbalanced translocations on the other hand, can be easily be mapped, although detecting the precise breakpoint again depends on the coverage.

When needed, single cell NGS coverage can be increased, for instance by reducing the number of sequencing libraries per sequencing run. This will not limitlessly increase resolution though, as the library complexity of a single cell library is limiting. To increase library complexity, the single cell genome can be amplified though whole genome amplification (WGA) before the library preparation. WGA does come with a risk of amplification bias, which can result in under- and overrepresented genomic regions in the

final alignment 64,65_{. Such regions, or even whole chromosomes, could then incorrectly be}

called as aneuploid; therefore optimization is required before using WGA in single cell sequencing.

Single-cell strand sequencing (Strand-seq)

An alternative method to map translocation breakpoints and inversions is Strand-seq. This method was originally developed to map sister chromatid exchanges (SCEs) and study sister chromatid inheritance patterns for each chromosome. During cell division, each daughter cell inherits one sister chromatid from each parental chromosome pair. Strand-seq enables researchers to uncover this inheritance pattern. For this purpose, cells are cultured in the presence of bromodeoxyuridine (BrdU), a thymidine analogue, for exactly one cell cycle resulting in BrdU to be incorporated only in the newly synthesized DNA strands. Libraries are then prepared from FACS-sorted single cells followed by an UV-Hoechst treatment step to induce nicks at the sites of BrdU incorporation in the newly formed strand. Therefore, only the original template strand is amplified in the subsequent PCR amplification. Importantly, the resulting libraries maintain directionality so that the reads map to their parental strand after sequencing. The bioinformatic pipeline BAIT (Bioinformatic Analysis of Inherited Templates) can

then be used to further annotate the reads 66_{. Maintaining directionality allows for the}

detection of sister chromatid exchanges that occurred during the cell division in the presence of BrdU, which can be visualized by BAIT. These sister chromatid exchanges are visible as switches in the template strand inheritance pattern. Similarly, Strand-seq can also be used to map translocations and inversions allowing more detailed karyotyping, even at lower sequencing depth.

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Taken together, single cell sequencing provides opportunities to accurately karyotype all chromosomes on single cell level, although the use of Strand-seq is limited to dividing

cells61,67_{. With the constant deceasing sequencing costs, single-cell sequencing or}

Strand-seq, while for now still costly, has the potency to become a high throughput karyotyping method both in a research as well as a diagnostics setting.

Conclusions

Aneuploidy is a feature of several syndromes associated with developmental and cognitive defects, a hallmark of cancer, and it could play a role in neurodegenerative disease. To better understand the relationship between aneuploidy and these pathologies, reliable methods to analyse karyotypes are a necessity. In this review we have provided an overview of existing techniques for karyotyping and highlighted their strengths and limitations. Typically, the most affordable methods require dividing cells, in which case aneuploidy rates are only representative of the dividing subpopulation, which might not represent aneuploidy rates in the whole cell population. Furthermore, dividing cells are sometimes simply not available, for instance when assessing post-mitotic tissues. While methods that can quantify aneuploidy rates in interphase cells can be used to circumvent this bias, most of these methods cannot detect aneuploidies at the single cell levels or are limited to analysis of a few chromosomes per cell and therefore karyotype heterogeneity remains obscured. Therefore, novel techniques such as single-cell sequencing might combine the best of both worlds: the ability to determine karyotypes at high resolution in an unbiased and high throughput fashion.

ACKNOWLEDGEMENTS

We thank the members of the Lansdorp and Foijer labs for fruitful discussion and Jorge Garcia Martinez and Klaske Schukken for critically reading the manuscript. FF is supported by Stichting Kinder Oncologie Groningen (SKOG) and Dutch Cancer Society grant RUG 2012-5549.

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48 | Chapter 2

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