Integrating soil imaging with spatial omics to uncover root-soil interactions.

Abstract

Soils exhibit remarkable spatial heterogeneity in environmental conditions, which plants perceive at the levels of the whole root system, individual roots, and root tissues. Cropping practices aimed at reducing the environmental footprint of agriculture are likely to intensify this heterogeneity, highlighting the urgent need to adapt crops to heterogeneous soil environments. Recent advances in soil imaging and spatial omics offer unprecedented opportunities to decipher the molecular, physiological, and ecological processes that underpin plant-soil interactions. In this review, we explore the substantial yet largely untapped potential of integrating soil imaging with spatial omics to uncover the fundamental mechanisms that control root foraging in heterogeneous soils. We present an overview of key imaging and molecular approaches that have particular potential for revealing root foraging behavior. To demonstrate their capabilities for generating spatially explicit insights into root-soil interactions, we highlight selected case studies covering both biotic (beneficial and detrimental soil organisms) and abiotic (physical and chemical soil properties) factors. Finally, we outline a workflow for integrating spatial omics with soil imaging through vertical integration of experimental studies across levels of environmental complexity, coupled with predictive modeling. Unlocking the full potential of these approaches will require linking molecular, physiological, and ecological mechanisms at the root-soil interface to whole-plant growth and crop productivity. These fundamental insights into the edaphic drivers of root foraging will be essential for guiding crop adaptation to future, more heterogeneous soil environments.

Plant phenotyping relevance

根の探索行動や根系・根組織の状態を可視化・解析する土壌イメージング手法を空間オミクスと統合するレビューであり、植物表現型取得・解析の方法論が中心です。

Code and data availability

This is a review article on integrating soil imaging with spatial omics. The supplied blocks contain no public phenotype/trait datasets, plant images, author analysis code, trained models, or supplements specific to this paper; all datasets and methods discussed belong to cited prior studies, and no code or data de- or

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