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

Lim, Eun-Cheon; Kim, Jae Il; Park, Ji Hye; Kim, Eun-Jung; Kim, Ju-Hyun; Park, Yeongmi; Cho, Hyun Seob; Byun, Dohwan; Henderson, Ian; Copenhaver, Gregory P.; Hwang, Inseok; Choi, Kyuha

doi:10.1111/tpj.14543

Cited by 19 publications

(22 citation statements)

References 44 publications

(80 reference statements)

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“…This assay measures crossover frequency and interference specifically in male meiosis 31 . For analysis we used a deep learning pipeline DeepTetrad, which enables high-throughput analysis of fluorescent tetrads 55 . We tested four three-color FTL intervals located in distal chromosome regions; I1bc, I1fg, I3bc and I5ab .…”

Section: Resultsmentioning

confidence: 99%

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HIGH CROSSOVER RATE1 encodes PROTEIN PHOSPHATASE X1 and restricts meiotic crossovers in Arabidopsis

et al. 2021

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Meiotic crossovers are tightly restricted in most eukaryotes, despite an excess of initiating DNA double-strand breaks. The majority of plant crossovers are dependent on Class I interfering repair, with a minority formed via the Class II pathway. Class II repair is limited by anti-recombination pathways, however similar pathways repressing Class I crossovers are unknown. We performed a forward genetic screen in Arabidopsis using fluorescent crossover reporters, to identify mutants with increased or decreased recombination frequency. We identified HIGH CROSSOVER RATE1 ( HCR1 ) as repressing crossovers and encoding PROTEIN PHOSPHATASE X1. Genome-wide analysis showed that hcr1 crossovers are increased in the distal chromosome arms. MLH1 foci significantly increase in hcr1 and crossover interference decreases, demonstrating an effect on Class I repair. Consistently, yeast two-hybrid and in planta assays show interaction between HCR1 and Class I proteins, including HEI10, PTD, MSH5, and MLH1. We propose that HCR1 plays a major role in opposition to pro-recombination kinases to restrict crossovers in Arabidopsis.

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Section: Resultsmentioning

confidence: 99%

“…For three-color, pollen-based FTL intervals we are able to measure crossovers in adjacent regions and thereby measure interference 31 , 55 . ( Fig.…”

Section: Resultsmentioning

confidence: 99%

HIGH CROSSOVER RATE1 encodes PROTEIN PHOSPHATASE X1 and restricts meiotic crossovers in Arabidopsis

et al. 2021

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“…The model developed using a deep learning approach has proved highly efficient for the identification of the stages of pollen development. In this way, the mean average precision of the model is within the appropriate values for this type of forecasting system [23,24]. For the specific doubled haploids' development application, a vacuolated microspore detection model would have been sufficient.…”

Section: Discussionmentioning

confidence: 92%

“…This generates a distribution that allows having a more accurate interpretation of the content of a specific anther. Regarding the specific precision values in some cell types, it should be noted that the lowest values correspond to those intermediate stages that are more difficult to differentiate, such as the case of medium microspores or young pollen, although even in this case, the correct identification values are over 80% [23,24]. On many occasions, the morphology of these cells is intermediate between the young and vacuolated microspore stages in the case of the medium microspore, or vacuolated and mature pollen in the case of young pollen.…”

Section: Discussionmentioning

confidence: 99%

“…For this reason, we present as an alternative the development of a model based on deep learning. These models are based on extracting statistical patterns from previously characterized pollen cells in different developmental stages [23]. With these patterns, a classifier model is fed to identify the position of the cells in the image and their stage of development.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning-Based System (Microscan) for the Identification of Pollen Development Stages and Its Application to Obtaining Doubled Haploid Lines in Eggplant

García‐Fortea

Garcı́a-Pérez

Gimeno-Páez

et al. 2020

Biology

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The development of double haploids (DHs) is a straightforward path for obtaining pure lines but has multiple bottlenecks. Among them is the determination of the optimal stage of pollen induction for androgenesis. In this work, we developed Microscan, a deep learning-based system for the detection and recognition of the stages of pollen development. In a first experiment, the algorithm was developed adapting the RetinaNet predictive model using microspores of different eggplant accessions as samples. A mean average precision of 86.30% was obtained. In a second experiment, the anther range to be cultivated in vitro was determined in three eggplant genotypes by applying the Microscan system. Subsequently, they were cultivated following two different androgenesis protocols (Cb and E6). The response was only observed in the anther size range predicted by Microscan, obtaining the best results with the E6 protocol. The plants obtained were characterized by flow cytometry and with the Single Primer Enrichment Technology high-throughput genotyping platform, obtaining a high rate of confirmed haploid and double haploid plants. Microscan has been revealed as a tool for the high-throughput efficient analysis of microspore samples, as it has been exemplified in eggplant by providing an increase in the yield of DHs production.

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High‐throughput classification of S. cerevisiae tetrads using deep learning

Szücs,

Selvan,

Lisby

2024

Yeast

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Meiotic crossovers play a vital role in proper chromosome segregation and evolution of most sexually reproducing organisms. Meiotic recombination can be visually observed in Saccharomyces cerevisiae tetrads using linked spore‐autonomous fluorescent markers placed at defined intervals within the genome, which allows for analysis of meiotic segregation without the need for tetrad dissection. To automate the analysis, we developed a deep learning‐based image recognition and classification pipeline for high‐throughput tetrad detection and meiotic crossover classification. As a proof of concept, we analyzed a large image data set from wild‐type and selected gene knock‐out mutants to quantify crossover frequency, interference, chromosome missegregation, and gene conversion events. The deep learning‐based method has the potential to accelerate the discovery of new genes involved in meiotic recombination in S. cerevisiae such as the underlying factors controlling crossover frequency and interference.

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

Cited by 19 publications

References 44 publications

HIGH CROSSOVER RATE1 encodes PROTEIN PHOSPHATASE X1 and restricts meiotic crossovers in Arabidopsis

HIGH CROSSOVER RATE1 encodes PROTEIN PHOSPHATASE X1 and restricts meiotic crossovers in Arabidopsis

A Deep Learning-Based System (Microscan) for the Identification of Pollen Development Stages and Its Application to Obtaining Doubled Haploid Lines in Eggplant

High‐throughput classification of S. cerevisiae tetrads using deep learning

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