Traditionally, measuring soil quality parameters requires destructive sampling and laboratory analyses that are laborious, slow, or expensive. Similarly, root phenotyping requires time and labor intensive processing and scanning of root tissue to collect data such as root length density and root architecture . Advances in imaging have been able to offset some of these hands on analyses: high resolution RGB imaging can differentiate between soil types facilitating soil type detection, which can improve mapping and hence conservation efforts . New approaches that overcome the limitations of laboratory tests include thermal infrared imaging, which can be used to assess soil moisture distribution and hydraulic properties and inform land surface models . Near infrared spectroscopy has been used for rapid and accurate identification of soil total nitrogen , organic matter , and pH levels in soil that can replace laboratory techniques . Similarly, hyperspectral imaging can be used to accurately provide TN, OM, and organic content information in various soils as well as fungal viability based on pixel spectra specific to browned, damaged, and undamaged tissue types . Because image processing of HSI is more challenging than that of RGB imaging, the two technologies can be used in tandem; for example, to optimize comprehensive analyses of soil and root systems in rhizoboxes . The accuracy of both IR and HSI can be improved by applying extreme learning machine models, which were previously used to increase the accuracy of soil moisture and surface temperature measurements . Because UAVs are scalable and programmable,vertical plant rack we expect that drone usage in phytobiome research will move toward autonomous UAV fleets that can monitor extensive fields with an array of cheaper and more accurate sensors.
We also expect aerial monitoring to be more closely coupled to robotics on the ground that could aid in conducting soil and plant analysis and deployment and maintenance of local sensor networks among various other tasks. Thus far, the development of robotics to measure soil characteristics has primarily focused on applications in environments that are difficult or unsafe to access. For instance, a robot was developed for measuring soil strength over depth, which is normally manually measured using a penetrometer, in unsafe zones . The Mars Phoenix Lander returned in situ measurements of Mars soil temperature, generated a topography map using imaging, and excavated soil samples for downstream testing .Plant microbiome signaling and metabolism rely on exchange of a large diversity of metabolites derived from microorganisms, plants, and the soil environment. Metabolomic methods enable direct characterization of these small molecules from soils and the various biological components. Given the large diversity of compounds that reside intra and extracellularly in these systems, mass spectrometry coupled to chromatography such as liquid chromatography MS and gas chromatography MS have become primary methods for chemical analysis. Both techniques are well suited for identification and quantification of a wide range of molecules found in biological and environmental samples by coupling the physical separation of the compounds using LC with the separation and analysis of ions using MS mass.GC MS typically has higher resolving power and produces richer fragmentation spectra, which makes it particularly well suited for identifying molecules such as small glycans that are often difficult to characterize by LC MS. It is also well suited for volatile molecules and poorly ionizing molecules that are often lost or not detected by LC MS.
LC MS, on the other hand, is better suited for thermally labile compounds and is a technique of choice for analysis of novel compounds. Typically, these approaches are suitable for identification of several hundreds of metabolites based on spectral databases and authentic standards . However, they are currently far from comprehensive, and improving metabolite identification is an important goal of metabolomics research. A number of studies have used MS based metabolomics to examine the chemical exchanges within phytobiomes; for example, the signaling molecules that direct the establishment of bacterial and mycorrhizal pathogens or symbionts with host plants. A number of metabolites have been identified, including sugars, amino acids, organic acids, phenolic compounds, and plant hormones, that are associated with beneficial interactions and are also implicated by single strain and whole community approaches . Exometabolite profiling methods have been used to examine root exudates and their function in recruiting soil bacteria . O’Banion et al. have reviewed the function of the main chemical constituents of plant microbe signaling. Similarly, chemical imaging of solutes in soils has been reviewed . Although MS imaging is a powerful and promising technique , it is extremely difficult to identify organic components from complex environmental samples due to chemical complexity of these samples and the lack of physical separation of compounds prior to ionization. New developments in using ion mobility to separate ions within mass spectrometers have tremendous potential to overcome these limitations and enable direct analysis of metabolites from tissues and environmental samples .It is well known that phytobiomes are affected by plant growth form and life history , plant community composition and habitat of origin, and even host plant species . In fact, there is growing evidence of that intraspecific variability of plant hosts produces variability in phytobiomes . Genetic differences within host species can affect microbe recruitment, community assembly, and, ultimately, the composition of phytobiomes.
As such, the phytobiome can be considered an extended phenotype of the plant that is determined by host genetics, the environment, and their complex interaction. Here, the standard tools of quantitative genetics can be used to study the phytobiome. For example, family experimental designs or kinship based mixed models can be used to partition variation in microbial abundance or composition into genetic and environmental components of variance for an entire assemblage of microbes associated with a particular plant compartment. This approach can provide insight into the host genetic architecture of the plant microbiome and, potentially, help to identify classes of microbes with close affinities for specific genotypes within a population. A number of recent publications have documented genetic variation within plant species for aspects of the microbiome, including providing estimates of heritability for overall microbial community diversity and richness and for the abundance of specific microbial taxa based on counts derived from amplicon sequencing,growing strawberries vertical system for example The majority of such studies have focused on crop plants in agronomic settings and little is known about the heritability of microbes from more natural populations; one exception to this is the outdoor study of Bergelson et al. . We imagine that some of these host genetic effects are related to available habitat for microbial establishment , to resources shared with microbes as root exudates, or from more complex immune responses in the plant. Incorporating host genetics in plant microbiome studies is promising because it will point to mechanisms leading to beneficial or deleterious plant–microbe interactions, as well as leverage the growing resources available in plant genomics. In order to more efficiently develop and deploy improved plant varieties, it is valuable to identify the causal genes or genetic markers underlying agronomic traits and disease resistance . In addition, there is a need to understand the plant genes that influence the composition and function of the microbiome to improve our understanding and in order to maximize plant productivity. Two methods are commonly used to identify genes or markers associated with quantitative traits: quantitative trait locus mapping and genome wide association studies . Both approaches rely on genome wide scans for statistical association between polymorphic genetic markers and quantitative variation in a measured phenotype. In the case of phytobiomes, the phenotype of interest could be a feature of the aggregate microbial community or an estimate of the relative abundance of a specific taxon . A key distinction between these methods is that QTL mapping populations are derived from lines crosses and, therefore, represent experimentally structured populations, whereas GWAS focus on naturally occurring individuals. QTL mapping tends to have more power to detect true associations but reduced ability to localize effects in the genome because of limited recombination in a breeding population. In contrast, GWAS are frequently under powered, given limited sample sizes, but can yield remarkably fine scaled localization due to extensive historical recombination.
It can also be much faster to establish a GWAS population than a QTL population because there is no need to create recombinant progeny through complex breeding designs across multiple generations. However, GWAS requires dense markers and reliable controls for population structure and, at best, yields correlative results rather than causal inference as achieved with QTLs. Because, in QTL studies, fewer alleles and markers are analyzed using a randomized genetic background, statistical analysis can yield causal relationships between alleles and traits . Although both GWAS and QTL analyses establishing relationships between plant genetics and phenotypic traits are common, links between plant genetics and microbiome composition and function have been rare. The earliest studies utilizing this approach focused on plant related microbial diseases , including fungal, oomycete, and bacterial pathogens. More recently, studies utilizing the model plant Arabidopsis thaliana have been published that explore complete microbial communities based on 16S rRNA gene amplicon sequencing. For example, Horton et al. identified host loci that influence fungal and bacterial colonization density on leaves across an A. thaliana population in the field and found that loci encoding defense and cell wall integrity affect bacterial and fungal community variation, whereas loci that influence the reproduction of viruses, trichome branching, and morphogenesis affect bacterial species richness. Similarly, Wallace et al. looked at the leaf microbial communities across maize lines and found that functions related to short chain carbon metabolism, secretion, and nitrotoluene degradation primarily encoded by Methylobacteria spp. are heritable metabolic traits, and that few plant loci were found to be significantly associated. These studies provide an exciting glimpse of the potential importance of host genetic variation in the phytobiome and give a clear path to the identification of candidate genes. Future studies will help to define the groups of microbes with strong host impacts, as well as identify new genetic and metabolic pathways important in plant– microbe interactions. Although aggregate community metrics may be relatively straightforward to generate, they may be difficult to interpret and less meaningful than studies focused on individual microbial species. However, it is also unclear how to best define microbial taxa for counting—what inference can be made from amplicon sequence variants, traditionally defined operational taxonomic units, or gene content abundance derived from enrichment or metagenomic analyses? Finally, genome wide studies carry a heavy multiple testing burden due to dense testing both across genomes and also across multiple taxa or phenotypes. Care will need to be taken to limit false positives and misleading inferences—methods developed for other “omics” based quantitative genetic systems such as expression or metabolic QTL analyses may provide helpful directions as the field matures.In an effort to conduct plant microbiome research across biologically meaningful spatiotemporal scales and with increased control, a range of fabricated ecosystems are being developed. Experimental control and complexity are inversely related in plant microbiome research. At the most extreme, controlled laboratory experiments are often binary , whereas field experiments feature real world complexity that is difficult to replicate year by year. A new generation of experimental platforms of increasing complexity now allows for multi factorial insight, reproducibility, and increased statistical power. The concept of controlled environments for exploring plant ecophysiology dates back to the late 1940s, when Firits Went developed a Phytotron at Caltech , a “Climatron” in St. Louis, MO , and an ecophysiology lab at the Desert Research Institute, University of Nevada, Reno, which is now home to the recently developed EcoCELLs . Went’s work inspired the development of the EcoTron program at Centre National de la Recherche Scientifique, Montpellier, France , and the EcoTron at Imperial College London, United Kingdom . EcoTrons are large, fabricated ecosystems that consist of an above ground dome of approximately 40 m3 and a below ground chamber that contains a lysimeter that can hold 2 to 12 tons of soil . The canopy area is up to 2 m tall and allows work under natural light as well as under controlled or artificial light conditions. Both above and below ground compartments are equipped with arrays of sensors and instrumentation for environmental control. Using the EcoTron, simulations of a wide range of environmental scenarios under realistic conditions can be performed, while measurements important for ecosystem processes such as atmospheric and soil gas composition, temperature, and pH, among others, can be conducted.