Immediate visualization of recombination events and chromosome segregation defects in fission yeast meiosis

Abstract

Schizosaccharomyces pombe, also known as fission yeast, is an established model for studying chromosome biological processes. Over the years, research employing fission yeast has made important contributions to our knowledge about chromosome segregation during meiosis, as well as meiotic recombination and its regulation. Quantification of meiotic recombination frequency is not a straightforward undertaking, either requiring viable progeny for a genetic plating assay, or relying on laborious Southern blot analysis of recombination intermediates. Neither of these methods lends itself to high-throughput screens to identify novel meiotic factors. Here, we establish visual assays novel to Sz. pombe for characterizing chromosome segregation and meiotic recombination phenotypes. Genes expressing red, yellow, and/or cyan fluorophores from spore-autonomous promoters have been integrated into the fission yeast genomes, either close to the centromere of chromosome 1 to monitor chromosome segregation, or on the arm of chromosome 3 to form a genetic interval at which recombination frequency can be determined. The visual recombination assay allows straightforward and immediate assessment of the genetic outcome of a single meiosis by epi-fluorescence microscopy without requiring tetrad dissection. We also demonstrate that the recombination frequency analysis can be automatized by utilizing imaging flow cytometry to enable high-throughput screens. These assays have several advantages over traditional methods for analyzing meiotic phenotypes.Electronic supplementary materialThe online version of this article (10.1007/s00412-019-00691-y) contains supplementary material, which is available to authorized users.

Inversions and integrations using CRISPR/Cas9 and a module-based plasmid construction kit for fission yeast

“…Integration of elements at different sites in the genome can be problematic when these contain identical or highly similar regions. This can be avoided by using sequences from other yeast species as shown by (Li et al 2019). We followed their example and designed a set of promoters with (a-b) overhangs for which the S. pombe versions had been tested (Russell 1989; Iacovoni et al 1999; Matsuyama et al 2008; Watt et al 2008; Verma et al 2014; Wang et al 2014).…”

“…To avoid potential non-homologous recombination with the inserted regions, we used sequences from other yeast species as shown by Li et al (2019). We used sequences from S. octosporus, S. japonicas or S. cryophilus for a set of promoters (a-b) for which the S. pombe versions had been tested (Russell 1989; Iacovoni et al 1999; Matsuyama et al 2008; Verma et al 2014; Wang et al 2014).…”

DeepTetrad: high‐throughput image analysis of meiotic tetrads by deep learning in Arabidopsis thaliana

“…The DeepTetrad system has the following advantages and potential applications to facilitate a wide array of studies: (i) DeepTetrad saves time and labor using highthroughput tetrad preparation, imaging, and automatic computational analysis (Table 1); (ii) DeepTetrad has enhanced reproducibility and accuracy by automatically excluding the individual variability inherent in manual tetrad counting and the effects of fluorescence silencing; (iii) DeepTetrad does not require the use of a sophisticated flow cytometer or high-purity pollen grains, instead it uses a rapid tetrad preparation method; (iv) DeepTetrad enables the detection of double COs by keeping the FTL in the qrt background; (v) In the future we plan to modify DeepTetrad to measure gene conversion rates using FTLs by changing the DeepTetrad classifier from tetrad classification mode to the detection of gene conversions (Francis et al, 2007); (vi) DeepTetrad also has the potential to be used for forward genetic screens of altered CO frequency and interference mutants; (vii) DeepTetrad measures CO frequency in defined intervals along the genome, whereas whole-genome sequencing can generate a genome-wide CO map at high resolution. However, DeepTetrad can help researchers quickly decide whether to generate genome-wide CO maps in diverse genetic backgrounds, a process that is expensive and time consuming; (viii) We anticipate that it will be possible to adapt it for use in other fluorescence-based tetrad analysis systems, including those that have been developed for Saccharomyces cerevisiae and Schizosaccharomyces pombe (Thacker et al, 2011;Li et al, 2019). Importantly, the high speed and accuracy of DeepTetrad result from its ability to recognize and classify tetrad images from mixtures of monads, dyads, triads, and tetrads.…”

Catalase T-Deficient Fission Yeast Meiocytes Show Resistance to Ionizing Radiation

“…To generate deletion cassettes for pcl1 and ctt1 we followed a previously described strategy [38]. For the pcl1∆-producing construct, the backbone and the dominant drug resistance marker (hphMX4) of pAG32 [39] after a PvuII-EcoRV digest were merged with a 398 bp upstream (oligonucleotides oUA600 5 -ATAGAACGCGGCCGCCAGAGCACTTATTTGTGGGC-3 and oUA601 5 -CAGCGTACGAAGCTTCAGCGATAGTGTAGAGGTAGTGATTG-3 ) and a 426 bp downstream flanking sequence (oligonucleotides oUA602 5 -CTCGAATTCATCGATGA TGCCAAACGTCTAAAGAGGG-3 and oUA603 5 -GCCACTAGTGGATCTGATCTTTCACCAT CACAGTCTCG-3 ) amplified by PCR from S. pombe genomic DNA (strain ALP1596 and ALP714, respectively) in a single NEBuilder assembly reaction; this resulted in the vector pALo243.…”