Vogel, J. T. | Search bioRxiv

bioRxiv2024

Nondestructive Seed Genotyping via Microneedle-Based DNA Extraction

Crop breeding plays an essential role in addressing food security by enhancing crop yield, disease resistance, and nutritional value. However, the current crop breeding process faces multiple challenges and limitations, especially in genotypic evaluations. Traditional methods for seed genotyping remain labor-intensive, time-consuming, and cost-prohibitive outside of large-scale breeding programs. Here, we present a handheld microneedle (MN)-based seed DNA extraction platform for rapid, nondestructive, and in-field DNA isolation from crop seeds for instant marker analysis. Using soybean seeds as a case study, we demonstrated the use of polyvinyl alcohol (PVA) MN patches for the successful extraction of DNA from softened soybean seeds. This extraction technology maintained high seed viability, showing germination rates of 82% and 79%, respectively, before and after MN sampling. The quality of MN-extracted DNA was sufficient for various genomic analyses, including PCR, LAMP, and whole genome sequencing. Importantly, this MN patch method also allowed for the identification of specific genetic differences between soybean varieties. Additionally, we designed a 3D-printed extraction device, which enabled multiplexed seed DNA extraction in a microplate format. In the future, this method could be applied at scale and in-field for crop seed DNA extraction and genotyping analysis.

plant biology↗

bioRxiv2023

Monitoring of drought stress and transpiration rate using proximal thermal and hyperspectral imaging in an indoor automated plant phenotyping platform

BackgroundThermography is a popular tool to assess plant water use behavior, as plant temperature is influenced by transpiration rate, and is commonly used in field experiments to detect drought stress. Its application in indoor automated phenotyping platforms is still limited and mainly focuses on differences in plant temperature between genotypes or treatments, instead of estimating stomatal conductance or transpiration rate. In this study, the transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated. In addition, the added value of combining thermal infrared (TIR) with hyperspectral imaging to monitor drought effects on plant transpiration rate (E) was evaluated. ResultsThe sensitivity of commonly used TIR indices to detect drought-induced and genotypic differences in water status was investigated in eight maize inbred lines in the automated phenotyping platform PHENOVISION. Indices that normalized plant temperature for vapor pressure deficit and/or air temperature at the time of imaging were most sensitive to drought and could detect genotypic difference in the plants water use behavior. However, these indices were not strongly correlated to stomatal conductance and E. The canopy temperature depression index, the crop water stress index and the simplified stomatal conductance index were more suitable to monitor these traits, and were consequently used to develop empirical E prediction models by combining them with hyperspectral indices and/or environmental variables. Different modeling strategies were evaluated including single index-based, machine learning and mechanistic models. Model comparison showed that combining multiple thermal infrared indices in a random forest model can improve E prediction accuracy, and that the contribution of the hyperspectral data is limited when multiple indices are used. However, the empirical models trained on one genotype were not transferable to all eight inbred lines. ConclusionOverall, this study demonstrates that existing TIR indices can be used to monitor drought stress and develop E prediction models in an indoor setup, as long as the indices normalize plant temperature for ambient air temperature or relative humidity.

plant biology↗