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Polyploidy induction of raspberry species (Rubus) using colchicine and oryzalin

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  • Raspberry (Rubus spp.) is a fruit crop used in many products across the United States. Commercial production of raspberry in North Dakota is challenging due to lack of cultivars with winter hardiness. In this study, two diploid red raspberry cultivars 'Prelude' and 'Polana' that are reported hardy in USDA hardiness zone 5 were used to induce polyploid raspberries that may have benefits of greater growth vigor, enhanced stress tolerance, larger fruit size, and higher yield. The effects of cultivar, anti-mitotic agent and its concentration, and the treatment duration on in vitro and ex vitro polyploidy induction were determined. 'Prelude' succeeded in polyploidy induction through the colchicine treatment of in vitro explants (apical and nodal buds) and ex vitro root dipping. There were three verified tetraploid 'Prelude' plants. One tetraploid plant of 'Prelude' was developed when in vitro explants were treated with 160 mM colchicine for 96 h and the other two were developed when the root system was dipped in the 10 mM colchicine for 8 h. No polyploid plants of 'Polana' were developed. Oryzalin, another anti-mitotic agent, did not succeed in polyploidy induction in this study. This study provides useful information on polyploidy induction in raspberry species, which could benefit future germplasm enhancement of raspberry and other Rubus species.
  • The seed industry is fundamental to ensure food security and basic to realize agricultural modernization[1]. Seed vigour is one of the important indicators to evaluate seed quality, which is a comprehensive evaluation of seed activity intensity and characteristics during germination and seedling emergence, including seed germination percentage, seedling emergence rate, seedling growth potential and uniformity, plant resistance and production potential.[2,3]. Seeds with high vigour have obvious growth advantages, fast emergence, strong stress resistance and high yield, while seeds with low vigour cannot emerge normally, or cannot grow normally after seedling emergence, with poor resistance, lack of seedlings and broken monopoly and have low yield[4]. Therefore, it is very important to evaluate seed vigour quickly and accurately and select seeds with high vigour for agricultural production.

    In recent years, the research and development of rapid and non-destructive seed quality testing technology has gradually become a new trend in the field of seed quality testing. This is due to some drawbacks of the current conventional standard testing methods, such as destructive, and time-consuming testing procedures that are highly dependent on the professional technology and relevant experience of analysts[5]. Non- destructive methods of testing have evaluated the seed quality according to a range of characteristics such as morphology, internal structure, chlorophyll content and chemical composition. Specifically, there are mainly different methods based on chemical properties (e.g., electronic noses), electrical properties (e.g., dielectric properties), acoustic properties (e.g., acoustic signals) and optical properties. There were a number of studies on non-destructive and rapid testing of seed vigour by using RGB image technology, X-ray testing technology, near infrared spectroscopy testing technology (NIR), hyperspectral technology and multispectral technology based on optical properties, and have made some progress[610].

    Red-Green-Blue (RGB) imaging technology have been applied to seed germination ability and vigour testing studies by capturing macroscopic phenotypic changes in seeds (e.g., size, shape, colour, surface texture, etc.)[6,11], but this method was difficult to distinguish genetically and phenotypically similar seeds[12]. X-ray detection technology uses ultra-high X-ray penetration to explore the correlation between the internal morphology of seeds and physiological potential, so as to label and sort inferior seeds[13]. Nevertheless, the interpretation of X-ray images is considered a subjective and tedious task by seed analysts[14]. The wavelength range of near-infrared spectroscopy (NIS) is 780−2526 nm, and the chemical components within the seed such as water, oil, fibre compounds and fatty acids have absorption peaks in this band[15]. However, NIR technique uses diffuse reflectance integrating sphere sampling. Its spot can only project to a limited area and the sensitivity is low, so the obtained spectral data cannot represent the whole seed sample[16]. Hyperspectral imaging technology is characterized by super multi-band, high spectral resolution and combination of atlas, showing a high classification accuracy in predicting rice seed vigour[9]. Its spectral range contains important bands for identifying phytochromes inside seeds[17]: chlorophyll a (430 nm, 662 nm and 680 nm) and chlorophyll b (448 nm and 642 nm)[18], but suffers from problems of overlapping spectral information and more redundant bands, which will affect the efficiency and accuracy of later modeling to a certain extent[19].

    Multispectral Imaging (MSI) is an emerging non-destructive physical measurement technique that combines computer vision and spectroscopy, and can quickly and accurately determine surface characteristics such as surface color and texture of the target object as well as detecting the intrinsic chemical composition and uses spectral reflectance to show differences and variations[20]. It has the characteristics of simple operation, high sensitivity, fast data extraction and non-destructive to the target object. It has been used in non-destructive testing studies on the seed vigour of leguminous crops such as alfalfa (Medicago sativa), soybean (Glycine max) and cowpea (Vigna unguiculata)[10, 2124]. In addition, there were many studies on the application of this technology in the seed health testing of Gramineae[2528] but few reports on the testing of seed vigour in Gramineae.

    Smooth bromegrass (Bromus inermis) is a perennial plant of Bromus genus of Gramineae. It is known as the 'king of forage grasses' because of its excellent characteristics of drought resistance, cold resistance, salt tolerance, high yield, good palatability and high nutritional value. Seed quality is the most important characteristic affecting seed value, and mainly reflected by seed vigour. Therefore, timely testing of the vigour of smooth bromegrass seed is important for its storage, transportation and preservation and utilization of germplasm resources. At present, there is no standard method for the determination of seed vigour of smooth bromegrass in the International Rules for Seed Testing[29], The Specification of Seed Testing for Forage and Turfgrass-Accelerated Aging for Vigour Test[30] is the only industry standard issued in China that includes seed vigour testing method for smooth bromegrass. Most domestic scholars have applied this method to the germination testing of smooth bromegrass seed[31,32], but there were some shortcomings such as long test period, tedious operation steps and damage to seeds. The objective of this study was to explore and evaluate the capability and feasibility of identifying and testing smooth bromegrass seed vigour at different maturity levels and different harvest years based on multispectral imaging technology, and to lay the foundation for exploring the application of multispectral imaging technology in the seed vigour standard testing and other important seed quality indicators.

    'Yuanye' smooth bromegrass seeds from different harvest years (2014, 2017 and 2019) and different maturity stages (milk-ripening, wax-ripening and full-ripening) were used as experimental materials. This variety was introduced from Canada and the seeds were provided by Beijing Zhengdao Seed Company. In 2013, we set up a seed production field for 'Yuanye' smooth bromegrass at the Experimental Station of China Agricultural University, Chengde State Yuershan Pasture, Hebei Province, China (41°44' N, 140°16' E), at an altitude of 1460 m. The site belongs to the semi-arid continental monsoon climate zone, with an average annual temperature of 0.5 °C, summer temperature of 17.4 °C and annual precipitation of 366.93 mm. The seed production field area was 0.52 hm2, row spacing was 30 cm, the actual sowing volume was 38.5 kg·ha−1, sowing with compound fertilizer, seed mixing volume was 190 kg·ha−1, and compound fertilizer N-P2O5-K2O content was 15%. The smooth bromegrass seeds harvested in 2014, 2017 and 2019 were all sown in 2013 and stored in the Forage Seed Laboratory of China Agricultural University at 4°C. Smooth bromegrass seeds of different maturity levels were observed and sampled in the field in July 2021. Smooth bromegrass is a panicle mostly with six whorls, named from bottom to top (Supplemental Fig. S1). The 1st and 2nd whorls as the lower layer, the 3rd and 4th whorls as middle and the 5th and 6th whorls as the upper layer. It was observed that flowering of smooth bromegrass started from the middle layer and spread towards the upper and lower layers. Within each spikelet, seeds are numbered from the base upwards (Supplemental Fig. S2). Through field observation and comparing the length, width and dry weight of different seeds, we found that the 1st and 2nd seeds were at the same maturity level. Therefore, the sampling method of this experiment was determined to take the first and second seeds of a spikelet in the middle layer (3rd and 4th whorls i.e., where flowering began) of smooth bromegrass at milk-ripening, wax-ripening and full-ripening stages as experimental seed samples (Supplemental Fig. S3). At each maturity stage, 200 spikelets were taken from the field, a total of 400 seeds. The seeds of milk-ripening stage were collected on 23 July, 2021, wax-ripening stage were collected on 1 August, 2021, and full-ripening stage were collected on 11 August, 2021.

    The multispectral imaging instrument VideometerLab4 (Videometer A/S, Herlev, Denmark) was used to take the images of seed samples. Before photographing the seed samples, the instrument calibration and light setup were needed, which involved calibration of absolute reflectance with a light and dark reference object, and geometric alignment with a dotted plate. Excitation/emission combinations of 660/700 nm and 405/600 nm were then added to the default light settings to capture fluorescence images of chlorophyll a and b[33,34]. This was a completely independent analysis and the data obtained were not involved in the multivariate analysis. By placing the seed sample at the bottom of the instrument sphere, a high-resolution multispectral raw image of 2,056 × 2,056 pixels can be obtained within seconds. The obtained raw image included 19 different wavelengths (365−970 nm), and then operated on the raw image to distinguish seed samples from extraneous background. Afterwards, the object analysis tool (Blob tool) in the VideometerLab software was opened to obtain measurement indicators such as area, length-width ratio, color index, saturation, mean spectral reflectance and histogram of the pixel distribution for each seed.

    Four hundred seeds were randomly taken from each seed lot, 50 seeds were put in a Petri dish each time and then placed in the multispectral instrument to extract and export morphological data and spectral data from each seed. The germination test was conducted according to the order in which the seeds were placed in the Petri dish, so that the results of each seed germination test should correspond to its multispectral information data. The experiment was conducted in September 2021.

    Germination tests were conducted on smooth bromegrass seeds harvested in different years and the seeds harvested in 2021 with different maturity levels.[30] Four hundred seeds randomly taken from each lot were used for germination tests after obtaining their multispectral information, which were conducted in October and November 2021. Smooth bromegrass seeds were placed in 11.5 cm x 11.5 cm Petri dishes padded with three layers of moist filter paper, and germinated in an incubator at 15/25 °C with alternating light and dark for 8 h and 16 h. The first count of normal seedlings on the 7th day and the last count was on the 14th day. The normal seedlings, abnormal seedlings, fresh seeds and dead seeds were recorded at the last count, and the germination potential and germination percentage were calculated according to the following formula:

    Germination potential (%) = (number of normal seedlings at the first count / total number of seeds tested) × 100%;

    Germination percentage (%) = (number of normal seedlings at the end of germination / total number of seeds tested) × 100%;

    The germination percentage of seeds after artificial accelerated aging was determined according to Specification of Seed Testing for Forage and Turfgrass- Accelerated Aging for Vigour Test.[30] Seeds were spread evenly in the aging box and put into aging incubator, with a distance of 2.5−3.0 cm between the aging box and the inner wall of the aging incubator (LH-150S,Zhejiang Topu Yunnong Technology Co., China) and two adjacent aging boxes. At least 400 seeds were placed in an ageing box and aged at 42 °C and 90% to 100% relative humidity for 48 h. The seeds were removed immediately after ageing and germination test was carried out within 1 h. The results of the seed vigour determination were expressed as the percentage of normal seedling number to total number of seeds tested at the end of germination.

    The spectral information and morphological information of seeds collected by multispectral were used for several multivariate analyses as follows:

    Principal component analysis (PCA), as an exploratory technique for multivariate data analysis, is used to obtain the overview of system variation in data. To analyze the average spectral and morphological characteristics data of smooth bromegrass seeds with different maturity levels and harvest years, we selected the first two principal components based on exploring the variation between morphologic data and spectral data of smooth bromegrass seeds with different maturity levels and harvest years.

    Normalized canonical discriminant analysis (nCDA) is used as a transformation construction method and the calculations behind it are more like PCA, but more supervised. According to the spectral characteristics of seeds with different germination situations, we labeled them as different layers to generate a single band visual analysis image, which was used to read the data results.

    Three supervised classification discriminant analysis methods, support vector machine (SVM), linear discriminant analysis (LDA) and random forest (RF), were used to model the classification of smooth bromegrass seeds at different maturity levels and different harvest years. For the hyperparameters of the SVM model, the kernel function was chosen to be linear, the penalty factor c was set to 0.5 and the other hyperparameters were set to their default values. For the hyperparameters of the RF model, ntree was 500, mtry was 3, and the other hyperparameters were default values.

    The training process of the three classification models was as follows:19 spectral information and 14 morphological index data of three different maturity levels of milk-ripening, wax-ripening and full-ripening smooth bromegrass seeds and three different harvest years of 2014, 2017 and 2019 were used as classification characteristic information, respectively. The seeds were randomly divided into training and validation sets according to the ratio of 7:3. In other words, a total of 400 samples for each seed lot, 280 seeds were randomly selected as training samples and the remaining 120 seeds were used as validation samples to build SVM, LDA and RF recognition models for seeds of different maturity levels and different harvest years, respectively. Accuracy, Sensitivity, Specificity and Precision of the SVM, LDA and RF models were calculated using the confusion matrix obtained from the validation set, and these metrics were calculated as follows[35]:

    Accuracy = (TX + TY) / (TX + TY + FX + FY) × 100%;

    Sensitivity = TX / (TX + FY) × 100%;

    Specificity = TY / (TY + FX) × 100%;

    Precision = TX / (TX + FX) × 100%.

    In the formula, X and Y represent two samples, TX represents the X sample with correct prediction, TY represents the Y sample with correct prediction; FX represents the Y sample that is predicted to be an X sample; FY means sample X that is predicted to be sample Y.

    PCA, SVM, LDA and RF were implemented using the FactoMineR, e1071, MASS and random Forest packages in the R package, respectively. The nCDA analysis used the MSI-Transformation Builder function in VideometerLab Software V.3.22.0.

    The results of the standard germination test showed that the seed germination potential and germination percentage increased significantly with the increase of maturity. The germination potential increased from 7% to 91%, and the germination percentage of seeds at milk-ripening stage was only 46%, at wax-ripening stage 56% and at full-ripening stage it could rise to 94% (Fig. 1a). On the other hand, the seed germination potential and germination percentage decreased significantly with increasing length of post-harvest storage. Seeds harvested in 2019 had a germination percentage of 97% and a germination potential of 89%. The germination percentage of seeds harvested in 2017 had dropped to 8% after storage for four years, with only 1% germination potential, while the current germination potential and germination percentage of seeds harvested in 2014 were 0% (Fig. 1b).

    Figure 1.  Germination situation of smooth bromegrass seeds under different conditions. Data in (a) and (b) were the germination potential and germination percentage of smooth bromegrass seeds at different maturity levels and harvest years under standard germination test, respectively. Data in (c) and (d) were the germination percentage of smooth bromegrass seeds at different maturity levels and harvest years after artificial accelerated aging, respectively. Different lowercase letters represent significant difference between different treatments during the experimental period (p < 0.05). Bars represent the mean values of four replicates ± standard deviation (SD).

    The results of artificial accelerated aging test of seed vigour showed that there were significant differences in germination percentage among three different maturity levels. The seeds at the full-ripening stage had the highest germination percentage and the seeds at the wax-ripening stage had the lowest germination percentage (Fig. 1c). For the seeds of three different harvest years, the germination percentage of seeds harvested in 2014 and 2017 were 0%, which were low vigour seeds, and seeds harvested in 2019 were high vigour seeds (Fig. 1d).

    Fluorescence intensity maps of chlorophyll a and b captured by multispectral showed that among the seeds of different maturity levels, seeds at the milk-ripening and wax-ripening stages showed higher value of chlorophyll a, significantly higher than the full-ripening stage, and seeds at the full-ripening stage had the highest value of chlorophyll b (Fig. 2a & c). While among the seeds from different harvest years, the seeds harvested in 2019 had the highest content of chlorophyll a and chlorophyll b, and the values of chlorophyll a and chlorophyll b showed a gradient decrease with increasing storage year (Fig. 2b & d).

    Figure 2.  Chlorophyll a and chlorophyll b fluorescence intensity of smooth bromegrass seeds at different maturity levels and harvest years. Data in (a) and (b) were chlorophyll a fluorescence intensity of seeds at different maturity levels and harvest years, respectively. Data in (c) and (d) were chlorophyll b fluorescence intensity of seeds at different maturity levels and harvest years, respectively. Different lowercase letters represent significant difference between different treatments during the experimental period (p < 0.05). Bars represent the mean values of four replicates ± SD.

    Fourteen morphological characteristics of each seed lots were extracted by multispectral imaging, and seeds of different maturity levels and different harvest years were compared separately. The results showed significant differences in 11 indicators among seed lots of different maturity levels (p < 0.05), with significant differences in area, length-width ratio, shape parameters, color parameters and saturation among seed lots at different maturity levels, and in hue indicators, the seeds at milk-ripening stage were significantly higher than the seeds at wax-ripening stage and the full-ripening stage. There were 10 significant differences among seed lots in different harvest years (p < 0.05), among which there were significant differences in CIELab L* and CIELab B* of color parameters, saturation and hue, as well as in length-width ratio and shape parameters (Table 1).

    Table 1.  Comparative analysis of morphological characteristics of smooth bromegrass seeds with different maturity levels and different harvest years.
    CharacteristicDifferent maturityDifferent harvest years
    Milk-ripening stageWax-ripening stageFull-ripening stage201920172014
    Area (mm2)12.61 ± 2.095c12.89 ± 1.777b13.45 ± 1.668a15.87 ± 2.675b17.74 ± 2.845a16.14 ± 2.512b
    Length (mm)9.87 ± 0.901a9.65 ± 0.904b9.89 ± 0.744a12.55 ± 1.793b13.44 ± 2.004a12.58 ± 1.843b
    Width (mm)1.81 ± 0.278c1.90 ± 0.211b2.02 ± 0.212a2.19 ± 0.282b2.31 ± 0.283a2.16 ± 0.253b
    Length-width ratio0.18 ± 0.029c0.20 ± 0.024b0.20 ± 0.022a0.18 ± 0.0290.17 ± 0.0270.18 ± 0.039
    Compactness Circle0.18 ± 0.028c0.19 ± 0.023b0.20 ± 0.019a0.17 ± 0.025a0.17 ± 0.021b0.18 ± 0.036a
    Compactness Ellipse0.99 ± 0.008b0.99 ± 0.006a0.99 ± 0.008b0.96 ± 0.029b0.96 ± 0.025b0.97 ± 0.022a
    BetaShape_a1.73 ± 0.221c1.79 ± 0.182b1.92 ± 0.237a3.30 ± 1.8783.33 ± 2.0593.07 ± 1.415
    BetaShape_b1.58 ± 0.199c1.69 ± 0.174b1.78 ± 0.208a2.74 ± 1.1692.75 ± 1.0322.61 ± 0.847
    Vertical Skewness−0.07 ± 0.051c−0.04 ± 0.034a−0.06 ± 0.046b−0.10 ± 0.088−0.09 ± 0.076−0.09 ± 0.07
    CIELab L*46.28 ± 2.959b44.72 ± 3.395c47.06 ± 2.306a51.43 ± 2.578a51.06 ± 2.442b48.42 ± 2.047c
    CIELab A*6.17 ± 1.402b4.96 ± 1.443c7.94 ± 0.787a6.26 ± 0.73b7.71 ± 0.847a7.67 ± 0.823a
    CIELab B*17.78 ± 1.682b12.58 ± 3.04c18.58 ± 1.32a17.57 ± 1.658c20.18 ± 1.56a19.68 ± 1.879b
    Saturation19.06 ± 1.844b13.50 ± 3.132c20.66 ± 1.392a18.80 ± 1.738c21.79 ± 1.674a21.33 ± 1.932b
    Hue1.23 ± 0.066a1.17 ± 0.112b1.17 ± 0.03b1.23 ± 0.033a1.21 ± 0.028b1.19 ± 0.109c
    Note: Different lowercase letters in the same line indicate significant differences, while the same letters indicate no significant differences (p < 0.05). The number of repetitions for this experiment was n = 4 for each seed lot samples.
     | Show Table
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    Through multispectral data collection and analysis, the average reflectance of smooth bromegrass seeds at 570−645 nm in wax-ripening stage was completely separate from that of milk-ripening stage and full-ripening stage, and the average reflectance of smooth bromegrass seeds at 780−970 nm in full-ripening stage was differed considerably from that of milk-ripening stage and wax-ripening stage (Fig. 3a). The average reflectance of smooth bromegrass seeds harvested in 2014 at 450−690 nm was completely separate from those smooth bromegrass seeds harvested in 2017 and 2019, and the mean reflectance of smooth bromegrass seeds harvested in 2019 at 940−970 nm differed significantly from those in 2014 and 2017 (Fig. 3b).

    Figure 3.  Mean spectral reflectance of smooth bromegrass seeds at (a) different maturity levels and (b) harvest years.

    The results of the principal component analysis based on morphological and spectral features showed that for seeds at different maturity levels, the first two principal components explained only 53.6% of the original variance, and there was more overlap in the 95% confidence ellipses in the PC1/PC2 two-dimensional plots for seed samples at milk-ripening and wax-ripening stages. Comparatively speaking, there was less overlap between the seeds at full-ripening stage and seeds at the other two stages (Fig. 4a). For seeds from different harvest years, the first two principal components explained only 54.5% of the original variance and had a large overlap, this indicated that there was a high degree of similarity among samples and that PCA was not effective in clustering smooth bromegrass seeds of different maturity levels and different years (Fig. 4b). In addition, all spectral data were positively correlated with the principal component 1(PC1), with 490 nm, 515 nm, 540 nm and 690 nm contributing more to PC1. Morphological character data saturation, color parameter CIELab A* and CIELab B* were positively correlated with principal component 2(PC2) of smooth bromegrass seeds at different maturity levels (Supplemental Fig. S4a), while negatively correlated with PC2 of smooth bromegrass seeds at different harvest years (Supplemental Fig. S4b). In terms of the separation effect of smooth bromegrass seeds with different maturity levels, LDA could clearly distinguish the smooth bromegrass seeds of full-ripening, milk-ripening and wax-ripening stage, with the best distinction of full ripening stage seeds (Fig. 4c). For smooth bromegrass seeds of different harvest years, although the seeds harvested in 2014 and 2017 both had low vigour, they also had a good differentiation effect, and the seeds harvested in 2019 had the best discrimination with other seeds (Fig. 4d).

    Figure 4.  Multivariate analysis based on multispectral data of smooth bromegrass seeds. Principal component analysis of smooth bromegrass seeds at (a) different maturity levels and (b) harvest years. LDA model diagram of smooth bromegrass seeds at (c) different maturity levels and (d) harvest years.

    The LDA model had the best prediction effect on seed differentiation at different maturity levels and harvest years compared to the RF and SVM. The accuracy of LDA could reach 94.2% (milk-ripening vs wax-ripening stages), 99.2% (wax-ripening vs full-ripening stages) and 99.6% (milk-ripening vs full-ripening stages), respectively (SVM was 93.8%, 97.9% and 99.2%, RF was 87.5%, 98.3% and 97.1%), with an average accuracy of 97.7%, and the sensitivity, specificity and accuracy could also reach more than 96.0% on average (Table 2). At same time, LDA could also distinguish seeds at different harvest years, and the accuracy was able to reach 90.0% (2014 vs 2017), 99.2% (2017 vs 2019) and 97.5% (2014 vs 2019), respectively (SVM was 86.3%, 96.3% and 95.0%, RF was 77.9%, 89.2% and 85.8%), with an average accuracy of 95.6%, and the sensitivity, specificity and precision were high, which could reach more than 95.0% on average (Table 2).

    Table 2.  Prediction of smooth bromegrass seeds with different maturity levels and harvested in different harvest years by LDA, SVM and RF models.
    ModelIndexDifferent maturityDifferent harvest year
    M vs WW vs FM vs F2014 vs 20172017 vs 20192014 vs 2019
    LDASensitivity (%)96.699.299.291.698.396.6
    Specificity (%)91.799.2100.088.4100.098.3
    Precision (%)92.099.2100.088.6100.098.3
    Accuracy (%)94.299.299.690.099.297.5
    SVMSensitivity (%)95.898.3100.089.996.695.0
    Specificity (%)91.797.598.382.695.995.0
    Precision (%)91.997.598.383.695.895.0
    Accuracy (%)93.897.999.286.396.395.0
    RFSensitivity (%)85.798.397.581.589.989.1
    Specificity (%)89.398.396.774.488.482.6
    Precision (%)88.798.396.775.888.483.5
    Accuracy (%)87.598.397.177.989.285.8
    Note: M stands for milk-ripening stage, W stands for wax-ripening stage and F stands for full-ripening stage.
     | Show Table
    DownLoad: CSV

    In addition, we also predicted the germination of each smooth bromegrass seed according to the nCDA model in VideometerLab4 software, and the accuracy of the model prediction was verified and evaluated by the actual results after the standard germination test was completed. The standard germination results of seed samples at different maturity levels and harvest years were classified and recorded separately according to dead seeds (D), fresh ungerminated seeds (F), abnormal seedlings (A) and normal seedlings (N), and the respective prediction accuracies were calculated separately.

    The calculated nCDA prediction results showed that, regarding seed samples of different maturity levels, the prediction accuracy of dead seeds with no vigour could reach 93.7%, normal seedlings with high vigour could reach 91.1%, abnormal seedlings and fresh ungerminated seeds could reach 97.1% and 97.5%, but the sum of abnormal and normal seedlings, i.e., seeds capable of germination (A + N), only reached 85.8%. For seed samples from different harvest years, the prediction accuracy of dead seeds was 91.3%, the prediction accuracy of normal seedlings could reach 91.3%, the prediction accuracy of abnormal seedlings and fresh ungerminated seeds could reach 92.6% and 98.3%, but the prediction accuracy of seeds that could germinate (the sum of abnormal seedlings and normal seedlings) could reach 94.0% (Table 3).

    Table 3.  Germination prediction of smooth bromegrass seeds with different maturity levels and harvest years based on nCDA.
    SampleClassificationActual number of CCSCorrectly predicted number of CCSActual number of RSSCorrectly predicted number of RSSAccuracy of prediction (%)
    Maturity levelD30124789987793.7%
    F751193116597.5%
    A109701091109597.1%
    N78376941732491.1%
    A + N89281130821885.8%
    Harvest yearD76669143440491.3%
    F331197117698.3%
    A1291188110292.6%
    N41939078170591.3%
    A + N43138676974294.0%
    Note: CCS stands for Corresponding classification samples, RSS stands for Remaining sorted samples. N stands for normal seedlings, A stands for abnormal seedlings, F stands for fresh ungerminated seeds and D stands for dead seeds.
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    Overall, the SVM model was better than both LDA and RF models in predicting the germination of smooth bromegrass seeds harvested in different years. However, the accuracy of both LDA and SVM models for predicting normal seedlings, abnormal seedlings, fresh ungerminated seeds and dead seeds was above 90.0%, and the accuracy of predicting abnormal seedlings and fresh ungerminated seeds was the same, the prediction accuracy of LDA was up to 98.3% and SVM was up to 99.2%. For the seed samples of different maturity levels, it was similar to the prediction of germination of seeds harvested in different years. The accuracy of LDA model and SVM model for predicting fresh ungerminated seeds was higher, which were 98.9% and 99.2%, respectively. And the prediction accuracy of LDA model and SVM model for abnormal seedlings was more than 90.0%. However, for normal seedlings and dead seeds, the prediction accuracy of both models was lower. The prediction accuracy of normal seedlings was less than 70.0%, and that of dead seeds was less than 80.0% (Table 4).

    Table 4.  Germination prediction of smooth bromegrass seeds with different maturity levels and harvested in different years by LDA, SVM and RF models.
    ModelIndexDifferent maturityDifferent harvest years
    NAFDNAFD
    LDASensitivity (%)79.90012.488.40096.5
    Specificity (%)40.599.799.794.596.599.299.287.1
    Precision (%)72.60042.393.40092.8
    Accuracy (%)66.791.798.974.293.698.398.393.1
    SVMSensitivity (%)80.800086.00097.4
    Specificity (%)40.5100.0100.0100.097.0100.0100.084.1
    Precision (%)72.8NANANA94.1NANA91.4
    Accuracy (%)67.291.999.275.393.199.299.292.5
    RFSensitivity (%)79.10012.469.00093.0
    Specificity (%)39.799.7100.095.693.1100.0100.072.0
    Precision (%)72.10NA47.884.8NANA85.1
    Accuracy (%)65.891.799.275.084.499.299.285.3
    Note: N stands for normal seedlings, A stands for abnormal seedlings, F stands for fresh ungerminated seeds and D stands for dead seeds.
     | Show Table
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    According to the results of germination test and artificial accelerated aging test of smooth bromegrass seeds, the seed vigour was highest at the full-ripening stage among different developmental stages. Moreover, the smooth bromegrass seed vigour gradually decreased with increasing storage time in different storage years, which was the same as the results of Zhang et al.[22] on alfalfa seeds at different maturity levels and different storage years. In addition, our study showed that the maturity level of smooth bromegrass seeds was positively correlated with seed germination percentage, and this was in agreement with the results of Mao et al.[36] on the vigour of smooth bromegrass seeds at different stages of development.

    Chlorophyll is a fluorophore, and its fluorescence is used as a non-invasive tool to evaluate plant photosynthesis. Contrary to the principle used in plants, chlorophyll fluorescence usually had negative effects in seeds[37]. Immature carrot and tomato seeds emitted high chlorophyll fluorescence, and these seeds had lower physiological quality compared to less fluorescence seeds[33,38]. Chlorophyll fluorescence of kale seeds was negatively correlated with their germination percentage[39]. These results were consistent with our findings. We quantified chlorophyll fluorescence intensities a and b using autofluorescence-spectral images acquired at 660/700 nm and 405/600 nm excitation/emission combinations. The result of chlorophyll fluorescence imaging indicated that there was accumulation and degradation of chlorophyll molecules in both the growth and developmental stages of seeds. Smooth bromegrass seeds at milk-ripening stage had a lower germination percentage but showed a higher fluorescence intensity of chlorophyll a, indicating that the chlorophyll within the seeds harvested at this stage was not yet completely degraded, which was similar to the results of Kenanoglu et al.[40] on pepper seeds of different maturity levels. The chlorophyll a content of seeds remained at a high level at the wax-ripening stage and began to decrease significantly at the full-ripening stage, indicating that the degradation of chlorophyll a molecule mainly occurred at this developmental stage. The content of chlorophyll b molecule was still accumulated to a certain extent during the developmental stage of the seeds and began to degrade slowly during the storage stage after maturation. The fluorescence intensity of chlorophyll b varied with seed vigour during the developmental and storage stages of the seeds.

    According to the morphological data and spectral data of smooth bromegrass seeds, the projected area and aspect ratio of seeds increased with maturity levels, indicating a positive correlation between seed size and vigour at the seed development stage, which was consistent with the findings of Zhang et al.[22] on alfalfa; whereas there was no significant correlation between the size and vigour of smooth bromegrass seeds at different harvest years. CIELab is an international standard for colour inspection[41], consisting of a luminance parameter (L) and two colour parameters (A and B). The three parameters CIELab L*, CIELab A* and CIELab B* obtained by multispectral imaging could be applied to determine the maturity and viability of seeds[42]. The color parameters CIELab L*, CIELab A* and CIELab B* of smooth bromegrass seeds at different maturity levels were significantly different. CIELab L* and CIELab B* of seeds at three harvest years were significantly different, and CIELab L* values were positively correlated with seed vigour. Overall, it seemed that the two colour indicators (CIELab L* and CIELab B*) had greater potential in distinguishing smooth bromegrass seeds at different maturity stages or different harvest years. Due to the presence of various overlapping peaks and the complex chemical composition of the seeds, the spectral assignment of absorption bands and their interpretation is quite complex[43]. As mentioned above, plant pigments, water, oil, fiber compounds and fatty acids have maximum absorption peaks at specific wavelengths. So we speculated that the mean spectral reflectance of seeds with different vigour levels at 450~690 nm and 940~970 nm was quite different, which may be related to the change of seed color, chlorophyll content, lipid content and water content during seed growth and development and storage stages[10,43,44]. And differences in the mean reflectance of smooth bromegrass seeds at different maturity stages from 780 to 940 nm may be due to some chemical differences in seeds[10].

    Previous studies have shown that the combination of LDA and multispectral data gives superior results[21,22,35], and the same results were also shown in the present experiment. The LDA model outperformed PCA, SVM and RF in predicting seed vigour, with model error rates of less than 10.0% for smooth bromegrass seeds at different harvest years and less than 6.0% for seeds at different maturity levels. In terms of the prediction of seed germination, the prediction accuracy of LDA model for normal seedlings and dead seeds of smooth bromegrass in different harvest years was above 90.0%, while the prediction accuracy of LDA model for normal seedlings and dead seeds of smooth bromegrass at different maturity levels was lower than 75.0%. This may be due to the fact that the LDA model combined with multispectral was not suitable for the determination of seed viability, or the small range of wavelengths obtained by multispectral did not allow for deep collection of key information for evaluating seed viability resulting in the combined multivariate analysis methods not being able to distinguish well seeds of different viabilities. Therefore, in the application of seed vigour testing, we can consider the organic combination of different testing technologies, and the advantages complement each other, which can achieve more desirable results, and this will be an important development direction in non-destructive testing technology for seed quality.

    The nCDA algorithm is integrated into the VideometerLab4 multispectral imaging system and allows for rapid presentation of classification maps to visually distinguish seed vigour[23]. According to the nCDA prediction results, it could be seen that the effect of distinguish seeds with different maturity levels, different harvest years and different germination states of seeds was relatively ideal, which could reach more than 90.0%, and the average prediction accuracy could also reach more than 93.0%, respectively. It was able to achieve 91.1% and 91.3% accuracy in predicting normal seedlings, so nCDA was also ideal for predicting the germination percentage of smooth bromegrass seeds. In addition, the test results showed that nCDA had a high ability to predict dead seeds with an average accuracy of 92.5%, and had great potential for predicting seed vigour or viability of smooth bromegrass. The prediction accuracy of germinating components (the sum of abnormal and normal seedlings) of smooth bromegrass seeds at different maturity levels was lower than that of abnormal and normal seedlings, respectively. However, the prediction accuracy of germinating components at different harvest years was higher than that of abnormal and normal seedlings respectively. This may be due to the fact that abnormal and normal seedlings of different maturity levels could not be better grouped by the nCDA model, and abnormal and normal seedlings of different harvest years could be better grouped by this model.

    It is worth noting that in the results of the artificial accelerated ageing determination for seed vigour of smooth bromegrass in different harvest years, the germination percentage of seeds harvested in 2014 and 2017 was both 0%, suggesting that the vigour of the two seed lots was the same. However, there were differences in the germination percentage of seed harvested in 2014 and 2017 under standard germination tests, 0% and 8% respectively, indicating that the standard method of artificial accelerated ageing was not sensitive and accurate in the evaluating the seeds of low vigour and no vigour. In this experiment, multispectral imaging was used to collect the spectral and morphological information of smooth bromegrass seeds from two harvested years, 2014 and 2017, and the prediction accuracy of seeds from the two years in the LDA model was 90.0%, and the four morphological indicators of seed size, color parameters, saturation and hue obtained by multispectral also showed significant differences. Therefore, multispectral imaging was more sensitive and accurate in the evaluation and differentiation of seed vigour compared to artificial accelerated ageing methods, and could capture the subtle morphological and spectral differences between seed lots, which has a better application prospect for the evaluation and detection of seed vigour. In addition, multispectral was almost non-destructive to seeds in the whole process of collecting spectral and morphological information of smooth bromegrass seed samples, which played a very important role in the conservation and utilization of germplasm resources.

    Based on multispectral technology, by extracting morphological data and spectral information of seeds and combining with LDA model, the seed vigour of smooth bromegrass could be tested more accurately, and the seed viability and germination percentage of smooth bromegrass could also be tested more accurately by combining with nCDA model. The method above was applicable to the determination of seeds with different vigour levels caused by different maturity levels in the process of seed formation and development, and also to the determination of seeds with different vigour levels caused by natural aging in different storage years after physiological maturity, which has good applicability and representativeness. In conclusion, the results of this study confirmed the feasibility of multispectral imaging techniques combined with multivariate analysis methods to rapidly achieve differentiation of smooth bromegrass seed vigour, which has a good prospect in non-destructive testing for seed vigour.

    This research was supported by Reproduction and Industrialization Demonstration of High-quality Forage Seeds (2020-Pro-Sci-Tech in Inner Mongolia, China-Technology Innovation Center of Forage Seed Industry-3), and the earmarked fund for CARS (CARS-34).

  • The authors declare that they have no conflict of interest.

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    Hermanson AL, Dai W. 2023. Polyploidy induction of raspberry species (Rubus) using colchicine and oryzalin. Technology in Horticulture 3:22 doi: 10.48130/TIH-2023-0022
    Hermanson AL, Dai W. 2023. Polyploidy induction of raspberry species (Rubus) using colchicine and oryzalin. Technology in Horticulture 3:22 doi: 10.48130/TIH-2023-0022

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Polyploidy induction of raspberry species (Rubus) using colchicine and oryzalin

Technology in Horticulture  3 Article number: 22  (2023)  |  Cite this article

Abstract: Raspberry (Rubus spp.) is a fruit crop used in many products across the United States. Commercial production of raspberry in North Dakota is challenging due to lack of cultivars with winter hardiness. In this study, two diploid red raspberry cultivars 'Prelude' and 'Polana' that are reported hardy in USDA hardiness zone 5 were used to induce polyploid raspberries that may have benefits of greater growth vigor, enhanced stress tolerance, larger fruit size, and higher yield. The effects of cultivar, anti-mitotic agent and its concentration, and the treatment duration on in vitro and ex vitro polyploidy induction were determined. 'Prelude' succeeded in polyploidy induction through the colchicine treatment of in vitro explants (apical and nodal buds) and ex vitro root dipping. There were three verified tetraploid 'Prelude' plants. One tetraploid plant of 'Prelude' was developed when in vitro explants were treated with 160 mM colchicine for 96 h and the other two were developed when the root system was dipped in the 10 mM colchicine for 8 h. No polyploid plants of 'Polana' were developed. Oryzalin, another anti-mitotic agent, did not succeed in polyploidy induction in this study. This study provides useful information on polyploidy induction in raspberry species, which could benefit future germplasm enhancement of raspberry and other Rubus species.

    • Raspberry, belonging to the genus Rubus (known as brambles) in the Rosaceae family, is a fruit species producing small fruits that are usually marketed towards fresh produce, juice, jelly, jams, wine, desserts, tea, and processed or frozen products[1,2]. Raspberry is a woody perennial shrub with biennial canes. The current year cane is called primocane that becomes floricane in the next year. The floricane dies after it produces fruit. Based on the fruiting habit, raspberries can be classified into three types: floricanes that produce the fruit in summer, primocanes that produce the fruit in the fall, and double-cropping ones produce the fruit on both floricanes and primocanes. The raspberry fruit is an aggregate that can be red, black, purple, or yellow/golden with a large variety in fruit size[3]. A USDA (United States Department of Agriculture) survey showed that 6,758 hectares in the United States were dedicated to raspberry production and a total of 81,147 megagrams (89,450 tons) raspberry were produced in 2022 with a market value of 531 million US dollars[4].

      Commercial raspberry production in the US is mainly in the Pacific Northwest region including northern California, Washington, and Oregon[5]; however, the demand of locally grown produce/products that are sold at farmer's markets and direct markets has been increasing due to concerns over food quality, nutrition, and food safety[6]. Raspberry is one of the most popular fruit crops for local markets nationwide including North Dakota and other Upper Midwest area. Due to the harsh winters and short growing season in North Dakota, limited raspberry cultivars that are winter hardy with good quality and high yield fruits are available to local growers. The cold climate restricts raspberry production and loss of yield due to cold injury is often observed in the Midwest and Northeast part of the United States[7]. The short growing season in North Dakota also discourages growing primocane raspberry because a large number of berries cannot be harvested before the first fall frost that often occurs in early October (The authors' personal observation). Therefore, the development of cold hardy floricane raspberry or early ripening primocane raspberry with large and flavorful fruit may help increase local raspberry production in North Dakota and other cold regions. Polyploidy plants are those that carry more than two sets of chromosomes that are heritable[8]. Polyploids have been used for plant breeding since 1930s due to the breeding implications and benefits that the polyploidy may have, such as tolerance to pests and diseases, thicker/darker leaves, larger flowers, longer-lasting flowers[8], increased frost tolerance[9], drought tolerance[10], greater chlorophyll content, greater phenolic compound content, greater carotenoid content, increased photosynthesis rate[11], larger fruit size, and different fruit shape[12]. In fruit crop breeding, increasing fruit size and yield as well as improving resistance to biotic and abiotic stresses are often pursued. Thus, the objective of this study was to induce polyploidy of red raspberry cultivars 'Prelude' and 'Polana' using colchicine and oryzalin, two most commonly used anti-mitotic agents for chromosome doubling in plants. 'Prelude' was bred at Cornell University, New York, USA and patented in 2001 mainly for fresh market production[13]. 'Prelude' is one of the earliest-ripening red raspberry cultivars. The fruit on floricanes can be harvested as early as late June in North Dakota. 'Prelude' is a double-cropping cultivar that can also produce fruit on current year growth (primocanes). The fruit on primocanes can be harvested from early September to early November in North Dakota. Canes of 'Prelude' are winter hardy and no severe winter damages were observed in recent ten years in Fargo, North Dakota (USDA zone 4a, −30o F) (unpublished information). 'Polana' is a red raspberry cultivar bred in Poland at the Research Institute of Pomology and Floriculture[14]. 'Polana' is one of the earliest-ripening primocane raspberry cultivars. In North Dakota, the 'Polana' fruit are harvested from late August to late October, making it a good primocane raspberry in the cold region. The ploidy of 'Prelude' and 'Polana' was determined to be diploid (2n = 2x = 14) (unpublished information) using the chromosome counting method[15]. We expect that increase ploidy level of raspberry species may result in improvement of winter hardiness, fruit quality, and yield.

    • Two red raspberry (Rubus idaeus L.) cultivars, 'Prelude' and 'Polana', were used in this research. Tissue cultures of the raspberries were developed in Dr. Dai's lab at North Dakota State University (USA)[16]. Tissue cultures of the two cultivars were maintained by sub-culturing every four weeks in Murashige and Skoog medium (MS)[17] supplemented with 30 g·l−1 sucrose, 5.5 g·l−1 agar (Caisson Labs Inc., Smithfield, UT, 2009), and 3 g·l−1 activated charcoal (Merck KGaA, Darmstadt, Germany). Medium pH was adjusted to 5.7−5.8 before autoclaving at 121 °C and 15 psi for 15 min. All raspberry tissue cultures were maintained in the culture room with a photoperiod of 16/8 h of day/night length, a light intensity of 55 µmol·m2·s−1 PPF (photosynthetic photon flux), and a day/night temperature of 24/18 °C. Tissue culture conditions in this study were identical unless otherwise noted.

    • A total of ten concentrations of colchicine (0, 20, 40, 80, and 160 mM) and oryzalin (0, 80, 160, 320, and 640 µM) solutions were made before the treatment. In brief, 100 ml of liquid MS medium supplemented with 30 g·l−1 sucrose and 1 ml·l−1 PPMTM (Plant Preservative Mixture) (Plant Cell Technology, Inc., Washington, DC, USA) was added to a 300 ml Erlenmeyer flask and autoclaved at 121 °C and 15 psi for 15 min. After cooling, appropriate amounts of 100 mM colchicine (in DMSO) (Sigma-Aldrich, St. Louis, MO, USA) or 100 mM oryzalin came from premixed with a surfactant at 40.4% (Monterey Lawn and Garden, Fresno, CA, USA) were added to the flask to make the final concentration of each solution. Six apical buds (~1 cm) and six nodal buds (~1 cm) were collected from in vitro plants of 'Prelude' or 'Polana' and placed in each of ten flasks. The flasks were covered with aluminum foil, labeled, and placed on an SK-300 Lab Companion shaker (Jeio Tech Inc., Billerica, MA, USA) for a constantly shaking at 110 rpm. After each treatment duration ended (24, 48, and 96 h), two apical buds and two nodal buds were taken from each flask, triple rinsed in autoclaved water and patted dry. Treated buds were then transferred to the 100 ml baby food jar (Sigma-Aldrich, St. Louis, MO, USA) containing 25 ml of MS medium supplemented with 1 ml·l−1 of PPM, 30 g·l−1 sucrose, 5.5 g·l−1 agar, and 10 µM BA for shoot development in the tissue culture room.

      In vitro shoots developed from the bud were subjected to ex vitro rooting. Shoots were dipped in a commercial rooting powder Take Root® (United Industries Corporation, St. Louis, MO, USA) for 5 s and inserted into ProMix BX+ (Premier Horticulture Inc., Quakertown, PA, USA) in a 48-hole plug tray covered with a plastic lid. After 4 weeks, plants were being acclimatized by replacing the initial lid (hole-less) with a series of lids with an increasing number of holes on it during a period of four weeks. The rooted and acclimatized plants were subjected to determination of chromosome number.

      The experiment of in vitro bud treatment with colchicine and oryzalin was conducted in a randomized complete block design (RCBD) with four replicates in a 2 × 5 × 3 factorial arrangement of two cultivars (with nested tissue apical buds and nodal segments), five concentrations of each agent, and three treatment durations. There were 30 treatments and each treatment had four buds and was repeated four times; therefore, a total of 480 buds were used in this experiment.

    • Colchicine solutions for root dipping were prepared by adding 100 ml of distilled water into (300 ml) Erlenmeyer flask, then adding appropriate amount of the colchicine stock solution. Plants with roots of 'Prelude' and 'Polana' were pulled out from the ex vitro rooting medium. The lower leaves of the plant were removed and kept only the top 2−3 leaves. The plant roots were trimmed to 5 cm long, gently rinsed with running water for 1 min, and blotted dry. Ten ml of each of four colchicine solutions (0, 0.1, 1.0, and 10.0 mM) was added to a 50 ml disposable plastic tube (Corning Incorporated, Corning, NY, USA). Two plants were inserted into a tube placed in a test tube rack with the root submerged in colchicine solution for a 4 or 8 h colchicine treatment. The tube rack was covered with aluminum foil. There were 16 treatments (2 cultivars × 4 concentrations × 2 durations) and three tubes per treatment (treated as replication). After treatment, plant roots were rinsed three times with distilled water in three 300 ml magenta boxes with 10-min intervals. Rinsed plants were then planted in a seedling tray filled with ProMix BX+ mycorrhizae (Premier Horticulture Inc., Quakertown, PA, USA). The trays were placed in the tissue culture room for plant re-growth.

      The ex vitro root dipping experiment was conducted as a RCBD with three replications in a 2 × 4 × 2 factorial arrangement with two cultivars ('Polana' or 'Prelude'), four colchicine concentrations (0, 0.1, 1.0, or 10.0 mM), and two durations (4 and 8 h). There were 16 treatments and each treatment had one plant with three replications; therefore, a total of 48 plants were used in this experiment.

    • New emerging shoot tips from the plants recovered from the chemical treatment of either in vitro buds or ex vitro root dipping were collected. The meristem of the shoot tip was squashed on a microscope viewing plate covered with a slide (VWR international, Randnor, PA, USA). Acetocarmine (Sigma-Aldrich, St. Louis, MO, USA) solution was used to stain the chromosomes. Chromosome detection was carried out on OLYMPUS CX41 microscope (Leeds Precision Instruments Inc., Minneapolis, MN, USA) at 40× view. After, plants confirmed to be polyploidy were prepared for DAPI (Vectashield®, Burlingame, CA, USA) image capture following the seven-step process. Step 1, slides with a good image of chromosome profile were dipped in liquid nitrogen for 10 s till the nitrogen stopped boiling around the slide. Step 2, put the slide down on a flat surface and pop off the cover slide using a blade on the corner. Step 3, put the frozen slide into 70% ethanol first for 5 min and air dry using clean blotting paper (Fisher Scientific, Pittsburgh, PA, USA) and repeated the previous step with 95% and 100% ethanol solutions (Decon Labratories Inc., King of Prussia, PA, USA). Step 4, after slide dried, load 13 µl of the DAPI antifade mounting solution to where the original center of the slide was, placed fresh cover slide flat on the DAPI droplet, and covered with a blotting paper. Step 5, one min after the DAPI antifade mounting solution spread, pressed gently on the corner of the cover slide to remove excess solution. Step 6, placed the finished slide into a shadow box to protect the DAPI dye from light, and then stored in the fridge until the image capture, and Step 7, took the shadow box with prepared slides into a dark room and captured the image with OLYMPUS BX51 (Olympus America Inc., Center Valley, PA, USA).

    • Data were collected after new bud growth was detected. The percent of initial buds developing new shoots were recorded as the survival rate. The number of plants with double the chromosome number of all survived plants was recorded as the polyploidy induction rate. Data in the in vitro bud treatment were tested for normality assumptions using PROC UNIVARIATE and homogeneity of variances among main factors (cultivar, chemicals, concentration, tissue type) were tested using PROC GLM for Hovtest (i.e. Levene). All data were analyzed by PROC GLIMMIX (SAS version 9.0401M6, SAS Institute, Cary NC, USA) for survival and polyploidy rates that were separated by the LSMEANS for factors and their interactions with Options lines (α = 0.05) for simple effects differences. Since PROC GLIMMIX was used to specify for specific distribution for correct analysis, only Type III tests of fixed effects for each response variables of ANOVA were calculated rather than the sum of squares and their means square.

    • Results of the in vitro bud treatment showed that the effect of cultivar on the survival rate was significant, whereas no significance was found in either concentration or treatment duration of both colchicine and oryzalin. There were no significant interactions between the cultivar and the concentration and treatment duration of colchicine or oryzalin on explant survival rate (Table 1) and polyploidy induction (Table 2).

      Table 1.  Type III tests of fixed effects of cultivar, concentration, and duration on the explant survival of red raspberry cultivars.

      Source of variationNumerator
      degrees of
      freedom
      Denominator
      degrees
      of freedom
      F ValueP > F
      Colchicine
      Cultivar12014.150.0430*
      Concentration42010.770.5443
      Duration260.710.4700
      Cultivar × Concentration42010.470.5867
      Cultivar × Duration22010.280.2800
      Concentration × Duration82010.680.6800
      Cultivar × Concentration × Duration82010.800.8000
      Oryzalin
      Cultivar12027.580.0064*
      Concentration42020.710.5860
      Duration261.280.2807
      Cultivar × Concentration42021.410.3146
      Cultivar × Duration22020.460.6292
      Concentration × Duration82020.390.9267
      Cultivar × Concentration × Duration82020.490.8631
      * Significant at α < 0.05.

      Table 2.  Type III tests of fixed effects of cultivar, concentration and duration on polyploidy induction of red raspberry cultivars.

      Source of variationNumerator
      degrees
      of freedom
      Denominator
      degrees
      of freedom
      F ValueP > F
      Colchicine
      Cultivar1670.320.3209
      Concentration4670.410.4139
      Duration2NANANA
      Cultivar × Concentration4670.410.4139
      Cultivar × Duration2NANANA
      Concentration × Duration8NANANA
      Cultivar × Concentration × Duration8NANANA
      Oryzalin
      Cultivar1NANANA
      Concentration4NANANA
      Duration2NANANA
      Cultivar × Concentration4NANANA
      Cultivar × Duration2NANANA
      Concentration × Duration8NANANA
      Cultivar × Concentration × Duration8NANANA
      NA: SAS could not compute data analysis due to no polyploidy values being present at different treatment levels.

      The effect of cultivar on the survival rate in colchicine treatments was significantly different (Table 1). 'Prelude' had a better survival rate (12.95%) than 'Polana' (7.08%) across all colchicine treatments (Table 3). In the oryzalin treatment, the effect of cultivar on the explant survival rate was also significant (Table 1). Unlike the cultivar response to colchicine, 'Polana' (14.17%) has a higher survival rate than 'Prelude' (6.15%) across all oryzalin treatments (Table 3). There were no significant interactions among oryzalin concentration, cultivar, and treatment duration on explant survival rate (Table 1). Due to no polyploidy plants being produced with oryzalin, no probability rate was calculated in SAS ANOVA (Table 2).

      Table 3.  Effect of cultivar on the explant survival rate in the colchicine and oryzalin treatment.

      CultivarSurvival rate (%)
      Colchicine treatmentOryzalin treatment
      'Prelude'12.95a*6.15b
      'Polana'7.08b14.17a
      * The same letter followed behind indicates no significance at α < 0.05.

      A tetraploid plant of 'Prelude' was identified from the treatment of 160 mM colchicine for 96 h with a sample size of 16 plants. Chromosome counting indicated that the plant contains 28 chromosomes in the meristem cell (2n = 4x = 28) (Fig. 1).

      Figure 1. 

      DAPI comparison of (a) diploid 'Prelude' (2n = 2x =14) and (b) tetraploid 'Prelude' (2n = 4x = 28). Bar = 10 µm.

    • Results showed that there were no significant differences in plant survival rate among cultivar, colchicine concentration, and treatment duration. No interactions between these factors were detected (Table 4). There was a trend of decreasing survival rate as an increase of colchicine concentration (Table 5). However, significant effects of cultivar (α = 0.1) and colchicine concentration (α = 0.5) on polyploidy induction were detected and an interaction between cultivar and concentration was also significant at α = 0.05 (Table 6).

      Table 4.  Type III tests of fixed effects of cultivar, concentration, and treatment duration on the plant survival in the root dipping treatment of raspberry cultivars.

      Source of variationNumerator
      degrees of
      freedom
      Denominator
      degrees of
      freedom
      F ValueP > F
      Cultivar13201.0000
      Concentration3321.890.1513
      Duration1320.330.5677
      Cultivar × Concentration3320.220.8802
      Cultivar × Duration1321.330.2568
      Concentration × Duration3321.000.4055
      Cultivar × Concentration × Duration3320.670.5787

      Table 5.  The effect of colchicine concentration on plant survival rate in root dipping treatment of raspberry.

      Colchicine (mM)Plant survival rate (%)
      0100a*
      0.1100a
      1.097.92a
      1089.58a
      * The same letter followed behind indicates no significance at α < 0.05.

      Table 6.  Type III tests of fixed effects of cultivar, concentration, and treatment duration on the polyploidy induction in the root dipping treatment of raspberry cultivars.

      Source of variationNumerator
      degrees of
      freedom
      Denominator
      degrees of
      freedom
      F valueP > F
      Cultivar1324.000.0540**
      Concentration3324.000.0159*
      Duration13201.0000
      Cultivar × Concentration3324.000.0159*
      Cultivar × Duration13201.0000
      Concentration × Duration33201.0000
      Cultivar × Concentration × Duration33201.0000
      * Significant at α < 0.05. ** Significant at α < 0.1.

      In the root dipping study, a polyploidy induction rate of 16.67% was achieved in the treatment of 10 mM colchicine for 'Prelude' (Table 7), indicating the different response of cultivars to colchicine. The result also indicates that an increase of colchicine concentration is needed for future polyploidy induction in red raspberry cultivars when ex vitro root dipping method is used.

      Table 7.  The effect of cultivar × colchicine concentration on the polyploidy rate in root dipping treatment of raspberry cultivars.

      Colchicine (mM)Polyploidy induction rate (%)
      Cultivar ‘Prelude’Cultivar ‘Polana’
      00b*0b
      0.10b0b
      1.00b0b
      1016.67a0b
      * The same lowercase letter indicates no significance at α < 0.05.
    • A polyploidy plant is a plant that carries more than double sets of chromosomes that are heritable[8]. Polyploidy plants possess certain traits, such as tolerance to pests and diseases[8], increased frost tolerance[9], drought tolerance[10], increased photosynthesis rate[11], larger fruit size, and different fruit shape[12], that benefit raspberry breeding and germplasm enhancement. A survey on the field-grown tetraploid red raspberry seedlings showed variations in flower size and fertility, fruit size, seed size, and leaf morphology between diploid and tetraploid raspberries[18]. Such polyploidy benefits could be utilized to potentially increase the plant yield quickly compared to standard breeding procedures[8]. The Rubus species are cytologically complex and their ploidy levels range from 2x to 14x with the basic chromosome number being seven[1921]. However, in the subgenus Idaeobatus (raspberries), polyploidy is not common and most species are diploid[21] as well as in raspberry cultivars[22]. In this study, a polyploidy induction system was developed through testing two anti-mitotic agents, colchicine and oryzalin and two red raspberry cultivars 'Prelude' and 'Polana'. The ultimate goal of this research was to develop polyploid raspberries with enhanced winter hardiness, good yield, and fruit quality that can be used as new cultivars or unique germplasm for breeding new raspberries for the Upper Midwest and other cold regions of the United States.

      In this study, two different ways were adopted for polyploidy induction: in vitro and ex vitro. In the in vitro way, both apical and lateral (nodal) buds were collected from in vitro plants and treated with colchicine or oryzalin under in vitro conditions. Treated buds were then placed back to tissue culture medium for recovery. New shoots grew from the bud were ex vitro rooted and subject to chromosome counting. In the ex vitro way, in vitro shoots were ex vitro rooted and acclimated. Polyploidy induction was conducted by dipping the root system in a colchicine solution under ex vitro conditions. Treated plants were recovered ex vitro and subject to chromosome counting. This study indicates that polyploidy induction in raspberry species can be achieved from both in vitro and ex vitro methods. However, differences in cultivars responding to the anti-mitotic agents and their concentrations were found. One tetraploid plant of 'Prelude' was produced in the in vitro treatment of 160 mM colchicine at 96 h duration that was a combination of the highest concentration and longest treatment time. No polyploid plants were developed from other colchicine treatments and from all treatments of colchicine and oryzalin for 'Polana', indicating the differential responses of raspberry genotypes to anti-mitotic agents and the concentration and treatment time, which agrees with findings in other research that polyploidy induction can be influenced by genotype[23]. Touchell et al.[8] reported that higher concentrations of colchicine and oryzalin lead to a greater polyploidy rate, which was confirmed in this research with colchicine. An increase of the concentration and/or the treatment time may result in a high polyploid induction rate in raspberry species, which should be considered in future research. The result from the root dipping treatment showed a higher polyploidy induction rate (16.67%) than in vitro bud treatment. Two tetraploid plants of 'Prelude' were developed from the treatment of 10 mM colchicine that was the highest colchicine concentration in the ex vitro root dipping treatment, indicating that increasing colchicine concentration is necessary for polyploidy induction using root dipping method. Although there were no significant factors affecting survival rate that might be caused by small sample number and less replicates, a trend where survival rate decreased with an increase of colchicine concentrations in root dipping was observed; therefore, increasing the sample and replicate numbers, optimizing concentration and treatment duration of colchicine (or oryzalin) is also necessary in future endeavor of polyploidy induction of raspberry species.

      In this study, all three tetraploid plants of 'Prelude' were induced by colchicine. While colchicine is the most used anti-mitotic agent for ploidy manipulation in plants, oryzalin, an herbicide, is also one of the anti-mitotic agents used for chromosome doubling, particularly in woody or semi-woody plants, including several species in the Rosacea family[8,24] although oryzalin had no success creating polyploidy of 'Polana' and 'Prelude' raspberry in this study. Thus, the applicability of oryzalin to ploidy manipulation of raspberry species needs to be further investigated because oryzalin is more cost efficient and environment friendly than colchicine.

      In conclusion, the effects of anti-mitotic agent (colchicine and oryzalin), treatment method, and treatment duration on polyploidy induction of two red raspberry cultivars were determined. A total of three tetraploid plants of 'Prelude' were induced using colchicine and their ploidy was verified using chromosome counting method. These tetraploid plants can potentially be used as new cultivars or breeding materials; therefore, this study can benefit germplasm enhancement as well as genetic understanding of raspberry and other Rubus species.

    • The authors confirm contribution to the paper as follows: study conception and design: Dai W, Hermanson A; data collection: Hermanson A; analysis and interpretation of results: Hermanson A, Dai W; draft manuscript preparation: Hermanson A, Dai W. Both authors reviewed the results and approved the final version of the manuscript.

    • All data generated or analyzed during this study are included in this published article.

    • The authors would like to thank Dr. Jawahar Jyoti for his help with statistical analyses.

      • The authors declare that they have no conflict of interest.

      • Copyright: © 2023 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (1)  Table (7) References (24)
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    Hermanson AL, Dai W. 2023. Polyploidy induction of raspberry species (Rubus) using colchicine and oryzalin. Technology in Horticulture 3:22 doi: 10.48130/TIH-2023-0022
    Hermanson AL, Dai W. 2023. Polyploidy induction of raspberry species (Rubus) using colchicine and oryzalin. Technology in Horticulture 3:22 doi: 10.48130/TIH-2023-0022

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