The molecular composition of soil organic matter (SOM) sets the foundation for terrestrial microbial community structures and carbon cycling dynamics. However, the specific chemical constituents of SOM are underexplored. In this chapter we present a protocol for the extraction of small molecule metabolites from soil followed by compound detection and identification using liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry. There are options within the protocol to assess either the extracellular pool of metabolites or the total pool (including intracellular) and either polar or nonpolar metabolites, depending on the reader's research interests. These methods can be followed individually for a more targeted analysis or all methods can be combined to obtain a more comprehensive understanding of SOM metabolite composition (such as amino acids, nucleobases, organic acids, fatty acids, carbohydrates, secondary metabolites, and antibiotics).

Plant health in natural environments depends on interactions with complex and dynamic communities comprising macro- and microorganisms. While many studies have provided insights into the composition of rhizosphere microbiomes (rhizobiomes), little is known about whether plants shape their rhizobiomes. Here, we discuss physiological factors of plants that may govern plant-microbe interactions, focusing on root physiology and the role of root exudates. Given that only a few plant transport proteins are known to be involved in root metabolite export, we suggest novel families putatively involved in this process. Finally, building off of the features discussed in this review, and in analogy to well-known symbioses, we elaborate on a possible sequence of events governing rhizobiome assembly.

Plants produce a diverse range of specialized metabolites that play pivotal roles in mediating environmental interactions and stress adaptation. These unique chemical compounds also hold significant agricultural, medicinal, and industrial values. Despite the expanding knowledge of their functions in plant stress interactions, understanding the intricate biosynthetic pathways of these natural products remains challenging due to gene and pathway redundancy, multifunctionality of proteins, and the activity of enzymes with broad substrate specificity. In the past decade, substantial progress in genomics, transcriptomics, metabolomics, and proteomics has made the exploration of plant specialized metabolism more feasible than ever before. Notably, recent advances in integrative multi-omics and computational approaches, along with other technologies, are accelerating the discovery of plant specialized metabolism. In this review, we present a summary of the recent progress in the discovery of plant stress-related specialized metabolites. Emphasis is placed on the application of advanced omics-based approaches and other techniques in studying plant stress-related specialized metabolism. Additionally, we discuss the high-throughput methods for gene functional characterization. These advances hold great promise for harnessing the potential of specialized metabolites to enhance plant stress resilience in the future.

Microbial consortia have the potential to perform complex, industrially important tasks. The design of microbial consortia requires knowledge of the substrate preferences and metabolic outputs of each member, to allow understanding of potential interactions such as competition and beneficial metabolic exchange. Here, we used exometabolite profiling to follow the resource processing by a microbial co-culture of two biotechnologically relevant microbes, the bacterial cellulose degrader Cellulomonas fimi, and the oleaginous yeast Yarrowia lipolytica. We characterized the substrate preferences of the two strains on compounds typically found in lignocellulose hydrolysates. This allowed prediction that specific sugars resulting from hemicellulose polysaccharide degradation by C. fimi may serve as a cross-feeding metabolites to Y. lipolytica in co-culture. We also showed that products of ionic liquid-treated switchgrass lignocellulose degradation by C. fimi were channeled to Y. lipolytica in a co-culture. Additionally, we observed metabolites, such as shikimic acid accumulating in the co-culture supernatants, suggesting the potential for producing interesting co-products. Insights gained from characterizing the exometabolite profiles of individual and co-cultures of the two strains can help to refine this interaction, and guide strategies for making this an industrially viable co-culture to produce valuable products from lignocellulose material.

EXECUTIVE SUMMARY Lawrence Berkeley National Laboratory (Berkeley Lab) scientists held a workshop at the DOE BER Genomic Sciences PI meeting on the use of Fabricated Ecosystems in Washington D.C. on February 25th, 2020. Ecosystem fabrication is an approach to creating controlled microbial, soil and plant ecologies within a laboratory setting that enable discovery and dissection of environmental variables, activities, and interactions. At Berkeley Lab, we are developing two systems which span spatial and temporal scales — the EcoFAB and the EcoPOD. The participants of the workshop discussed the potential applications and challenges of using fabricated ecosystems as tools to tackle BER-relevant scientific questions. Specifically, they identified (1) research challenges that would benefit from ready access to this infrastructure (2) identified and prioritized technical challenges that currently limit these systems and (3) discussed how to use them to bridge the gap between lab and field research. To ensure that the fabricated ecosystems , in particular the larger-scale EcoPODs, are of use to as much of the BER community as possible, the workshop participants made the following recommendations: 1. Produce publicly available datasets for a set of control variables. Use for benchmarking, and assessing reproducibility between systems. 2. Implement data standards. 3. Develop/leverage nano/micro sensors and in situ root imaging. 4. Encourage the development of interdisciplinary teams to develop EcoPOD experiments. 5. Develop fabricated ecosystems with size and complexity that sits between the EcoFAB and EcoPOD to accelerate use and access.

Mass spectrometry has become a choice method for broad-spectrum metabolite analysis in both fundamental and applied research. This can range from comprehensive analysis achieved through time-consuming chromatography to the rapid analysis of a few target metabolites without chromatography. In this review article, we highlight current high-throughput MS-based platforms and their potential application in metabolomics. Although current MS platforms can reach throughputs up to 0.5 seconds per sample, the metabolite coverage of these platforms are low compared to low-throughput, separation-based MS methods. High-throughput comes at a cost, as it's a trade-off between sample throughput and metabolite coverage. As we will discuss, promising emerging technologies, including microfluidics and miniaturization of separation techniques, have the potential to achieve both rapid and more comprehensive metabolite analysis.

Mass spectrometry imaging (MSI) has recently emerged as a widely used technique in application areas including pharmaceutical evaluation, ecosystem physiology, high-throughput screening and many others. Mass spectrometry images are made by raster scanning a sample in two dimensions and acquiring mass spectra at each location. Although there are many ways of performing MSI and a diversity of suitable samples, data analysis steps are generally consistent across experiments. These standard analysis steps include access to and management of the raw data, processing of individual spectra with centroiding and denoising algorithms. They also include normalization approaches to both improve spot-to-spot variability and quantification of molecules. Finally, multivariate statistics approaches are used to navigate the complexity of sometimes overwhelming large volume of information. Here, we present all of these analyses approaches and discuss how and why they are used in MSI.

Flatbed scanners are commonly used for root analysis, but typical manual segmentation methods are time-consuming and prone to errors, especially in large-scale, multi-plant studies. Furthermore, the complex nature of root structures combined with noisy backgrounds in images complicates automated analysis. Addressing these challenges, this article introduces RhizoNet, a deep learning-based workflow to semantically segment plant root scans. Utilizing a sophisticated Residual U-Net architecture, RhizoNet enhances prediction accuracy and employs a convex hull operation for delineation of the primary root component. Its main objective is to accurately segment root biomass and monitor its growth over time. RhizoNet processes color scans of plants grown in a hydroponic system known as EcoFAB, subjected to specific nutritional treatments. The root detection model using RhizoNet demonstrates strong generalization in the validation tests of all experiments despite variable treatments. The main contributions are the standardization of root segmentation and phenotyping, systematic and accelerated analysis of thousands of images, significantly aiding in the precise assessment of root growth dynamics under varying plant conditions, and offering a path toward self-driving labs.