Swartz Landon G, Liu Suxing, Dahlquist Drew, Kramer Skyler T, Walter Emily S, McInturf Samuel A, Bucksch Alexander, Mendoza-Cózatl David G
Department of Electrical Engineering and Computer Science, University of Missouri, 411 S 6th St., Columbia, Missouri, 65201, USA.
Division of Plant Science and Technology, C.S. Bond Life Sciences Center, University of Missouri, 1201 Rollins St., Columbia, Missouri, 65211, USA.
Plant J. 2023 Dec;116(6):1600-1616. doi: 10.1111/tpj.16449. Epub 2023 Sep 21.
The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., the dark genome). High-throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, access to commercial HTPP platforms remains limited. Here we describe the design and implementation of OPEN leaf, an open-source phenotyping system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf, coupled with our SMART imaging processing pipeline was able to consistently document and quantify dynamic changes at the whole rosette level and leaf-specific resolution when plants experienced changes in nutrient availability. Our data also demonstrate that VIS sensors remain underutilized and can be used in high-throughput screens to identify and characterize previously unidentified phenotypes in a leaf-specific time-dependent manner. Moreover, the modular and open-source design of OPEN leaf allows seamless integration of additional sensors based on users and experimental needs.
拟南芥基因组的初稿于20多年前发布，尽管进行了深入的分子研究，但仍有超过30%的拟南芥基因未被表征或没有指定功能。部分原因是基因家族内的基因冗余或基因的本质特性，即基因缺失会导致致死性（即暗基因组）。高通量植物表型分析（HTPP）提供了一种自动化且无偏的方法，用于表征由基因冗余或诱导性基因沉默产生的细微或瞬时表型；然而，商业HTPP平台的使用仍然有限。在此，我们描述了OPEN leaf的设计与实施，这是一个具有云连接和远程双边通信功能的开源表型分析系统，以促进数据收集、共享和处理。当植物经历养分可利用性变化时，OPEN leaf与我们的SMART成像处理管道相结合，能够在整个莲座叶水平和叶片特定分辨率下持续记录和量化动态变化。我们的数据还表明，可见光传感器的利用仍然不足，可用于高通量筛选，以叶片特定的时间依赖性方式识别和表征以前未识别的表型。此外，OPEN leaf的模块化和开源设计允许根据用户和实验需求无缝集成额外的传感器。
