Background

Eukaryotic cells are equipped with a set of DNA damage response mechanisms to defend themselves against internal and external stresses. External stresses, such as chemical agents cause damage or mutation to the DNA and, if left unrepaired, will propagate to the offspring and lead to genomic instability. Methyl methanesulfonate (MMS) is one example of a chemical (alkylating) agent that methylates DNA bases and stalls the replication fork, thereby generating double-strand breaks (DSBs) when the replication fork collapses (Arnaudeau et al., 2001). In Schizosaccharomyces pombe, DSBs are often repaired by the error-free homologous recombination (HR) pathway (Ferreira and Cooper, 2004). HR proteins, such as Rhp54, lead to foci formation in response to MMS treatment, with the number of foci formed increasing proportionately to the level of DNA damage (Maki et al., 2011). Recent work has shown that a mutation in the histone H3 methyltransferase Set2 can affect Rhp54 foci formation during MMS treatment, thereby suggesting a role for Set2 in MMS resistance (Lim et al., 2018).

Quantification of foci number can reflect the robustness of DNA repair mechanisms in cells. However, measuring foci formation efficiency using fluorescently tagged DNA repair proteins often involves manual counting, which is susceptible to human error and is time-consuming (Rübe et al., 2008; Lorenz et al., 2009). There are several free or commercially available software on the market that afford better accuracy in foci determination and increase the throughput; however, there are several problems with such software (Herbert et al., 2014; Ledesma-Fernández and Thorpe, 2015; Oeck et al., 2015; Wiesmann et al., 2015). First, there is lack of a fully systematic and user-friendly way to move from image acquisition to image processing to foci analysis (counting); indeed, a user-friendly program must allow the user to have more control during each step of the process without requiring a background in computer programming (scripts and macros). Second, most of the available software or plugins were developed based on foci formation in mammalian cells, particularly targeting γH2AX foci, and thus might not be suitable for determining foci formation in yeast cells, which are much smaller in size (Böcker and Iliakis, 2006; Jucha et al., 2010; Anderson et al., 2013). The FociQuant software has been used for foci quantification in yeast with few complications; this is because the protocol focuses on the kinetochore protein, which forms a single distinct focus due to the clustering of centromeres (Ledesma-Fernández and Thorpe, 2015). However, difficulties arise in the quantification of foci for HR proteins; because DSBs can occur anywhere in the genome, multiple foci can be scattered in the nucleus and in different focal planes. Therefore, we are unable to use pre-existing methods of foci quantification to determine the foci of HR proteins accurately.

To overcome these problems, we employed the Nikon microscope system for image acquisition and NIS-Elements Advance Research software (v5.02) with the Extended Depth of Focus (EDF) plugin and “General Analysis” for image processing and foci analysis, respectively. Similar to other advanced microscope systems, DAPI and GFP Z-stack images are captured in a single file with one mouse-click. Because the cells exist in a 3D conformation, the EDF function enables the focused image of each plane captured by the Z-stack to be combined into a single, focused image. In this way, foci at different focal planes are included, thereby increasing the accuracy of the quantification. Furthermore, the software enables automated foci quantification by first determining the nucleus via DAPI staining, followed by counting the number of GFP foci in it; this prevents the inclusion of other non-nucleus, GFP noise. Although the analysis process is automated, the user can manually deselect regions or noise that are incorrectly determined by the software. The control and commands on the software are user-friendly, and the user does not need to have programming knowledge to modify certain quantification steps. In addition, the software is not only suitable for the analysis of images captured by a Nikon microscope but can be used to analyze images taken from other sources through additional steps.

Here, using MMS-dependent activation and foci formation of GFP-tagged Rhp54 as an example, we present an unbiased and precise method for the quantification of foci number formed by Rhp54-GFP in yeast. We have applied this method in recent work (Lim et al., 2018) to count foci number in wild-type and set2-mutant cells following MMS treatment.