How to count chromosomes in a cell: An overview of current and novel technologies

Abstract

Aneuploidy, an aberrant number of chromosomes in a cell, is a feature of several syndromes associated with cognitive and developmental defects. In addition, aneuploidy is considered 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 methods to measure the chromosome numbers in cells 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 several "traditional" karyotyping methods as well as the latest high throughput next generation sequencing karyotyping protocols with their respective advantages and disadvantages.

Non-Random Mis-Segregation of Human Chromosomes

“…The standard approach to measure aneuploidy, manual scoring of chromosome number using Fluorescence In-Situ Hybridization (FISH) of centromere-targeted probes (Burrell et al, 2013;Fenech, 2007) is low-throughput and subject to significant artefacts (Faggioli et al, 2012;Knouse et al, 2014;Valind et al, 2013;van den Bos et al, 2016), limiting the resolution of previous efforts to examine biased mis-segregation (Brown et al, 1983;Evans and Wise, 2011;Fauth et al, 1998;Hovhannisyan et al, 2016;Spence et al, 2006;Torosantucci et al, 2009;Xi et al, 1997). New technologies such as next generation sequencing-based methods van den Bos et al, 2016) are still expensive and technically challenging (Bakker et al, 2015). To resolve this we analysed individual chromosome aneuploidy rates in a high throughput manner using a novel platform and in the absence of fitness effects and selection.…”

Aneuvis: Web-based exploration of numerical chromosomal variation in single cells

“…These results were supported by the FISH bivariate heatmaps, which show increased numerical chromosomal variation in senescent fibroblasts relative to young fibroblasts ( Supplementary Figure 3). Inconsistencies between FISH and scL-WGS in measuring aneuploidy are recognized, and likely due to a differential sensitivity of these techniques (FISH is prone to detection of false positive cells and scL-WGS is prone to false negative detection) (Bakker et al, 2015). However, the observed aneuploidy and heterogeneity scores were higher in senescent cells versus young fibroblasts for both FISH and scL-WGS inputs (Supplementary Figure 4), highlighting a trend toward increased numerical chromosomal variability in senescent cells that was present across both methods.…”

“…The first step is to quantify the number of chromosomes per cell. Multiple experimental techniques and computational tools exist for completing this step, and the final output is often a spreadsheet or text file that contains chromosomal copy number information for all nuclei analyzed (Bakker et al, 2015). The second step is to quantify the degree of numerical chromosomal variation.…”

CIN and Aneuploidy: Different Concepts, Different Consequences

“…[6] Additionally, stable aneuploid cell lines have been engineered by introducing one or more extra chromosomes to investigate the consequences of aneuploidy in the absence of CIN. [7,8] There are many methods to quantify aneuploidy, [9] but in order to quantify CIN actual mis-segregation rates need to be measured in living cells, which is time consuming and challenging, especially in animal models. Therefore, in many cases, when CIN is induced in a cell population or tissue, only the resulting aneuploidy is quantified as a measure of the underlying CIN.…”