@article{li_poonam_cui_hsieh_jagadeesan_xu_bruce_vogel_sessions_cabrera_et al._2025, title={Non‐destructive seed genotyping via microneedle‐based <scp>DNA</scp> extraction}, volume={23}, ISSN={1467-7644 1467-7652}, url={http://dx.doi.org/10.1111/pbi.70055}, DOI={10.1111/pbi.70055}, abstractNote={Summary 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 labour‐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, non‐destructive 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.}, number={6}, journal={Plant Biotechnology Journal}, publisher={Wiley}, author={Li, Mingzhuo and Poonam, Aditi Dey and Cui, Qirui and Hsieh, Tzungfu and Jagadeesan, Sumeetha and Xu, Jin and Bruce, Wesley B. and Vogel, Jonathan T. and Sessions, Allen and Cabrera, Antonio and et al.}, year={2025}, month={Mar}, pages={2317–2329} }
 @article{hossain_mainello-land_wang_mativenga_jamalzadegan_xu_ristaino_razavi_li_wei_2025, title={Smartphone-Based Colorimetric VOC Sensor for Early Detection of <i>Phytophthora Ramorum</i> in Rhododendrons}, url={https://doi.org/10.1021/acssensors.5c02872}, DOI={10.1021/acssensors.5c02872}, abstractNote={Traditional plant pathogen detection often relies on molecular technologies, which allow species-level detection but are often time-consuming. Plant volatile organic compounds (VOCs) have recently been harnessed to assist in disease detection and plant health monitoring. However, current VOC detection methods are unsuitable for field use due to the need for expensive laboratory equipment and slow processing times. To address this, we developed a portable paper-based colorimetric sensing technology for early detection of ramorum blight in rhododendron caused by <i>Phytophthora ramorum</i>. This colorimetric sensor array, which includes nanomaterials and organic dyes, was optimized to detect alcohol, terpene, and ester, key VOC biomarkers emitted by infected rhododendron leaves. Color quantification was done quickly by smartphone imaging. Principal component analysis (PCA) was used to cluster and classify individual plant volatiles. Our VOC sensing platform detected ramorum blight 2 days after inoculation, aligning with real-time loop-mediated isothermal amplification (LAMP) analysis. Moreover, the platform distinguished pathogen-induced VOCs from those produced by nonbiological stresses such as drought and mechanical damage. This noninvasive diagnostic technology demonstrates significant potential for disease detection in the field.}, journal={ACS Sensors}, author={Hossain, Oindrila and Mainello-Land, Amanda and Wang, Yan and Mativenga, Belinda and Jamalzadegan, Sina and Xu, Jin and Ristaino, Jean B. and Razavi, Seyedamin and Li, Fanxing and Wei, Qingshan}, year={2025}, month={Dec} }