Data_Sheet_3_Model-Based Design of Long-Distance Tracer Transport Experiments in Plants.ZIP

Studies of long-distance transport of tracer isotopes in plants offer a high potential for functional phenotyping, but so far measurement time is a bottleneck because continuous time series of at least 1 h are required to obtain reliable estimates of transport properties. Hence, usual throughput values are between 0.5 and 1 samples h−1. Here, we propose to increase sample throughput by introducing temporal gaps in the data acquisition of each plant sample and measuring multiple plants one after each other in a rotating scheme. In contrast to common time series analysis methods, mechanistic tracer transport models allow the analysis of interrupted time series. The uncertainties of the model parameter estimates are used as a measure of how much information was lost compared to complete time series. A case study was set up to systematically investigate different experimental schedules for different throughput scenarios ranging from 1 to 12 samples h−1. Selected designs with only a small amount of data points were found to be sufficient for an adequate parameter estimation, implying that the presented approach enables a substantial increase of sample throughput. The presented general framework for automated generation and evaluation of experimental schedules allows the determination of a maximal sample throughput and the respective optimal measurement schedule depending on the required statistical reliability of data acquired by future experiments.

针对植物体内示踪同位素长距离运输的研究，在功能表型组学（functional phenotyping）领域具备极高应用潜力，但目前测量时长仍是核心瓶颈：为获取可靠的运输特性估计值，需采集至少1小时的连续时间序列数据。因此，常规样本处理通量仅为0.5~1样本/小时。为此，本研究提出通过在单株植物样本的数据采集环节引入时间间隙，并采用轮转流程依次测定多株植物，以提升样本处理通量。与常规时间序列分析方法不同，机理型示踪运输模型（mechanistic tracer transport models）可支持中断式时间序列的分析。以模型参数估计的不确定性作为量化指标，可对比完整时间序列数据，评估中断采集所损失的信息总量。本研究搭建案例实验，系统探究了通量范围为1~12样本/小时的多种实验方案。研究发现，仅需少量数据点的优化实验设计即可满足参数估计的精度要求，这表明本方法可实现样本处理通量的大幅提升。本研究提出的自动化生成与评估实验方案的通用框架，可根据未来实验所需的数据统计可靠性，确定最大样本处理通量及其对应的最优测量流程。