Three transgenic HOSUT lines of winter wheat, HOSUT12, HOSUT20, and HOSUT24, each harbor a single copy of the cDNA for the barley sucrose transporter gene HvSUT1 (SUT), which was fused to the barley endosperm-specific Hordein B1 promoter (HO; the HOSUT transgene). Previously, flow cytometry combined with PCR analysis demonstrated that the HOSUT transgene had been integrated into different wheat chromosomes: 7A, 5D, and 4A in HOSUT12, HOSUT20, and HOSUT24, respectively. In order to confirm the chromosomal location of the HOSUT transgene by a cytological approach using wheat aneuploid stocks, we crossed corresponding nullisomic-tetrasomic lines with the three HOSUT lines, namely nullisomic 7A-tetrasomic 7B with HOSUT12, nullisomic 5D-tetrasomic 5B with HOSUT20, and nullisomic 4A-tetrasomic 4B with HOSUT24. We examined the resulting chromosomal constitutions and the presence of the HOSUT transgene in the F2 progeny by means of chromosome banding and PCR. The chromosome banding patterns of the critical chromosomes in the original HOSUT lines showed no difference from those of the corresponding wild type chromosomes. The presence or absence of the critical chromosomes completely corresponded to the presence or absence of the HOSUT transgene in the F2 plants. Investigating telocentric chromosomes occurred in the F2 progeny, which were derived from the respective critical HOSUT chromosomes, we found that the HOSUT transgene was individually integrated on the long arms of chromosomes 4A, 7A, and 5D in the three HOSUT lines. Thus, in this study we verified the chromosomal locations of the transgene, which had previously been determined by flow cytometry, and moreover revealed the chromosome-arm locations of the HOSUT transgene in the HOSUT lines.
Observing chromosomes is a time-consuming and labor-intensive process, and chromosomes have been analyzed manually for many years. In the last decade, automated acquisition systems for microscopic images have advanced dramatically due to advances in their controlling computer systems, and nowadays, it is possible to automatically acquire sets of tiling-images consisting of large number, more than 1000, of images from large areas of specimens. However, there has been no simple and inexpensive system to efficiently select images containing mitotic cells among these images. In this paper, a classification system of chromosomal images by deep learning artificial intelligence (AI) that can be easily handled by non-data scientists was applied. With this system, models suitable for our own samples could be easily built on a Macintosh computer with Create ML. As examples, models constructed by learning using chromosome images derived from various plant species were able to classify images containing mitotic cells among samples from plant species not used for learning in addition to samples from the species used. The system also worked for cells in tissue sections and tetrads. Since this system is inexpensive and can be easily trained via deep learning using scientists' own samples, it can be used not only for chromosomal image analysis but also for analysis of other biology-related images.