Protocols for In Vivo Doubled Haploid (DH) Technology in Maize Breeding: From Haploid Inducer Development to Haploid Genome Doubling

Aboobucker, Siddique I.; Jubery, Talukder Z.; Frei, Ursula; Chen, Yu-Ru; Foster, Tyler L.; Ganapathysubramanian, Baskar; Lübberstedt, Thomas

doi:10.1007/978-1-0716-2253-7_16

Cited by 7 publications

(4 citation statements)

References 33 publications

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“…In contrast, Ribeiro et al (2020) found that this method was not reliable when assuming short plants with lower leaf sheath as putative haploids at 8 days after sowing (DAS) since all putative haploids obtained with those criteria were confirmed to be true diploids regarding flow cytometry. For rogueing of potential false positives in haploid nurseries, it is advisable to perform multiple plant inspections with different selection intensities from two weeks after transplanting to early reproductive stage, as these visual characteristics are most noticeable near the V 6 vegetative phase of growth (Aboobucker et al, 2022). The innate differences between haploid (left) and diploid (right) individuals in maize at early seedling or 4 days after sowing (A), seedling in plug trays or 7-8 days after sowing (B), V2/V3 seedling or 10-14 days after sowing (C), and adult stages (D).…”

Section: Vegetative Stagementioning

confidence: 99%

Haploid identification in maize

Dermail,

Mitchell,

Foster

et al. 2024

Front. Plant Sci.

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Doubled haploid (DH) line production through in vivo maternal haploid induction is widely adopted in maize breeding programs. The established protocol for DH production includes four steps namely in vivo maternal haploid induction, haploid identification, genome doubling of haploid, and self-fertilization of doubled haploids. Since modern haploid inducers still produce relatively small portion of haploids among undesirable hybrid kernels, haploid identification is typically laborious, costly, and time-consuming, making this step the second foremost in the DH technique. This manuscript reviews numerous methods for haploid identification from different approaches including the innate differences in haploids and diploids, biomarkers integrated in haploid inducers, and automated seed sorting. The phenotypic differentiation, genetic basis, advantages, and limitations of each biomarker system are highlighted. Several approaches of automated seed sorting from different research groups are also discussed regarding the platform or instrument used, sorting time, accuracy, advantages, limitations, and challenges before they go through commercialization. The past haploid selection was focusing on finding the distinguishable marker systems with the key to effectiveness. The current haploid selection is adopting multiple reliable biomarker systems with the key to efficiency while seeking the possibility for automation. Fully automated high-throughput haploid sorting would be promising in near future with the key to robustness with retaining the feasible level of accuracy. The system that can meet between three major constraints (time, workforce, and budget) and the sorting scale would be the best option.

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Section: Vegetative Stagementioning

confidence: 99%

Haploid identification in maize

Dermail,

Mitchell,

Foster

et al. 2024

Front. Plant Sci.

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“…The inducer F 1 s were crossed with C1-I inducers to obtain inducer haploids. Inducer haploids were subjected to the regular protocol of DH technology in maize (Vanous et al, 2017;Aboobucker et al, 2022) to obtain DHI lines. In addition, the HIRs of five C1-I inducers were evaluated by crossing them with nine inducer F 1 s and BHI306 and B73_R1-nj inbred (Maize Genetic Stock center: X17D), which were used as checks in the summer of 2020.…”

Section: Application Of C1-i Inducers For R1-nj Inducer Developmentmentioning

confidence: 99%

Development of doubled haploid inducer lines facilitates selection of superior haploid inducers in maize

Chen,

Lübberstedt,

Frei

2024

Front. Plant Sci.

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Haploid inducers are key components of doubled haploid (DH) technology in maize. Robust agronomic performance and better haploid induction ability of inducers are persistently sought through genetic improvement. We herein developed C1-I inducers enabling large-scale in vivo haploid induction of inducers and discovered superior inducers from the DH progenies. The haploid induction rate (HIR) of C1-I inducers ranged between 5.8% and 12.0%. Overall, the success rate of DH production was 13% on average across the 23 different inducer crosses. The anthesis–silking interval and days to flowering of inducer F1s are significantly correlated with the success rate of DH production (r = −0.48 and 0.47, respectively). Transgressive segregants in DH inducers (DHIs) were found for the traits (days to flowering, HIR, plant height, and total primary branch length). Moreover, the best HIR in DHIs exceeded 23%. Parental genome contributions to DHI progenies ranged between 0.40 and 0.55, respectively, in 25 and 75 percentage quantiles, and the mean and median were 0.48. The allele frequency of the four traits from inducer parents to DHI progenies did not correspond with the phenotypic difference between superior and inferior individuals in the DH populations by genome-wide Fst analysis. This study demonstrated that the recombinant DHIs can be accessed on a large scale and used as materials to facilitate the genetic improvement of maternal haploid inducers by in vivo DH technology.

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“…The ability to sort seeds for embryo/ scutellum size is a significant value addition. Similarly, the nondestructive sorting of single seeds based on oil content (OC) has been shown to be useful for early-generation screening to improve the efficiency of breeding (Silvela et al, 1989;Xu et al, 2019) and for haploid selection in an oil-inducer-based doubled haploid breeding program (Chaikam et al, 2019;Aboobucker et al, 2022). Over the past few years, nuclear magnetic resonance (NMR) (Melchinger et al, 2017;Yang et al, 2018), fluorescence imaging (Boote et al, 2016), near-infrared (NIR) reflectance spectroscopy (Jiang et al, 2007;Armstrong et al, 2011;Jones et al, 2012;Gustin et al, 2020), hyperspectral imaging (Weinstock et al, 2006), and line-scan Raman hyperspectral imaging (Liu et al, 2022) have been developed to measure or predict oil content.…”

Section: Introductionmentioning

confidence: 99%

Self-supervised maize kernel classification and segmentation for embryo identification

et al. 2023

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IntroductionComputer vision and deep learning (DL) techniques have succeeded in a wide range of diverse fields. Recently, these techniques have been successfully deployed in plant science applications to address food security, productivity, and environmental sustainability problems for a growing global population. However, training these DL models often necessitates the large-scale manual annotation of data which frequently becomes a tedious and time-and-resource- intensive process. Recent advances in self-supervised learning (SSL) methods have proven instrumental in overcoming these obstacles, using purely unlabeled datasets to pre-train DL models.MethodsHere, we implement the popular self-supervised contrastive learning methods of NNCLR Nearest neighbor Contrastive Learning of visual Representations) and SimCLR (Simple framework for Contrastive Learning of visual Representations) for the classification of spatial orientation and segmentation of embryos of maize kernels. Maize kernels are imaged using a commercial high-throughput imaging system. This image data is often used in multiple downstream applications across both production and breeding applications, for instance, sorting for oil content based on segmenting and quantifying the scutellum’s size and for classifying haploid and diploid kernels.Results and discussionWe show that in both classification and segmentation problems, SSL techniques outperform their purely supervised transfer learning-based counterparts and are significantly more annotation efficient. Additionally, we show that a single SSL pre-trained model can be efficiently finetuned for both classification and segmentation, indicating good transferability across multiple downstream applications. Segmentation models with SSL-pretrained backbones produce DICE similarity coefficients of 0.81, higher than the 0.78 and 0.73 of those with ImageNet-pretrained and randomly initialized backbones, respectively. We observe that finetuning classification and segmentation models on as little as 1% annotation produces competitive results. These results show SSL provides a meaningful step forward in data efficiency with agricultural deep learning and computer vision.

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Protocols for In Vivo Doubled Haploid (DH) Technology in Maize Breeding: From Haploid Inducer Development to Haploid Genome Doubling

Cited by 7 publications

References 33 publications

Haploid identification in maize

Haploid identification in maize

Development of doubled haploid inducer lines facilitates selection of superior haploid inducers in maize

Self-supervised maize kernel classification and segmentation for embryo identification

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