Most of our current knowledge on plant molecular biology is based on experiments in controlled laboratory environments. However, translating this knowledge from the laboratory to the field is often not straightforward, in part because field growth conditions are very different from laboratory conditions. Here, we test a new experimental design to unravel the molecular wiring of plants and study gene–phenotype relationships directly in the field. We molecularly profiled a set of individual maize plants of the same inbred background grown in the same field and used the resulting data to predict the phenotypes of individual plants and the function of maize genes. We show that the field transcriptomes of individual plants contain as much information on maize gene function as traditional laboratory‐generated transcriptomes of pooled plant samples subject to controlled perturbations. Moreover, we show that field‐generated transcriptome and metabolome data can be used to quantitatively predict individual plant phenotypes. Our results show that profiling individual plants in the field is a promising experimental design that could help narrow the lab‐field gap.

13Most of our current knowledge on plant molecular biology is based on experiments in 14 controlled lab environments. Over the years, lab experiments have generated 15 substantial insights in the molecular wiring of plant developmental processes, stress 16 responses and phenotypes. However, translating these insights from the lab to the 17 field is often not straightforward, in part because field growth conditions are very 18 different from lab conditions. Here, we test a new experimental design to unravel the 19 molecular wiring of plants and study gene-phenotype relationships directly in the 20 field. We molecularly profiled a set of individual maize plants of the same inbred 21 background grown in the same field, and used the resulting data to predict the 22 2009; Atkinson and Urwin, 2012; Nelissen et al., 2014; Nelissen et al., 2019). It has 102 been advocated that to close this lab-field gap, more -omics data and associated 103 phenotypic data should be generated on field-grown plants (Alexandersson et al., 104 2014; Nelissen et al., 2019;Zaidem et al., 2019). Several pioneering studies have 105 already investigated how gene expression is related to environmental stimuli in the 106 field (Nagano et al., 2012; Richards et al., 2012; Plessis et al., 2015). Large-scale 107 studies relating field-generated transcriptomes to field phenotypes are however still 108 lacking. 109Here, we propose a new strategy for studying the wiring of plant pathways and traits 110 directly in the field, involving -omics and phenotype profiling of individual plants of the 111 same genetic background grown in the same field. Uncontrolled variations in the 112 micro-environment of the individual plants hereby serve as a perturbation 113 mechanism. Our expectation is that, in addition to stochastic effects, the individual 114 plants will be subject to subtly different sets of environmental cues, and will in 115 response exhibit different molecular profiles and phenotypes. The aim of this study is 116to investigate to what extent we can use such individual plant differences in the field 117 to link genes to biological processes and field phenotypes. Earlier, we found that 118 gene expression variations among individual Arabidopsis thaliana plants grown under 119 the same stringently controlled lab conditions contain a lot of information on the 120 molecular wiring of the plants, on par with traditional expression profiles of pooled 121 plant samples subject to controlled perturbations (Bhosale et al., 2013). If even gene 122 expression variability among lab-grown plants contains functionally relevant 123 information, the molecular and phenotypic variability among field-grown plants may 124 contain a wealth of information on processes occurring in the field. 125We profiled the ear leaf transcriptome, ear leaf metabolome and a number of 126 phenotypes for individual field-grown maize plants of the same inbred line (Zea mays 127 B104), and used the resulting data to predict the function of genes and to 128 quantitatively predict individ...

No abstract