Background

Root depth is a trait of great relevance in the adaptation of plants to changes in the content and distribution of nutrients in the soil. Shallow roots are pivotal for the uptake of nutrients more abundantly found in topsoil layers, like phosphorus [1–3], whereas deep roots are involved in the uptake of nitrates, which move with water [4,5]. Deep roots have also been shown to improve plant resilience to drought [6–8] and are a desirable trait for strategies that aim to fight climate change by increasing carbon storage in the soil [9–11]. Despite its enormous agricultural relevance, the genetic determinants of root depth are poorly understood. This is largely due to the current lack of methods suitable for mid- and large-scale studies of root depth in soil. While excavation methods (shovelomics) and imaging techniques like X-ray have proved useful to estimate the distribution of root biomass across soil depths in crop species, the amount of labor, the complexity of image analysis, the destructiveness of the methods, and their cost, among other factors, severely limit their use to rather small, validation experiments [12–17]. Such limitations are even more severe when the study involves species characterized by small, fragile roots like the model organism Arabidopsis. In recent years, at least two methods for the study of the Arabidopsis root system architecture (RSA) and root depth in soil have been published. The GLO-Roots method relies on the use of plants transformed to express the luciferase marker, which allows the imaging of roots growing in thin, soil-filled, transparent pots [18]. The GLO-RIA software is then used to analyze the images and extract traits such as depth [18]. In Ogura's method, whole pots containing the root system are sectioned in two halves at the center of the plant, and images of the exposed roots are obtained and processed for measurements of "shallowness" (standard deviation of all root pixels in x-axis direction in a picture divided by that in y-axis direction), a proxy for root depth [19]. Although both provide valuable insights into the genetic control of RSA traits and root depth in Arabidopsis, their applicability is restricted by the need to use transformants, in one case, and the considerable amount of labor, in the other.

We have recently developed ClearDepth, a conceptually simple, low-cost, and robust method to evaluate root depth in soil [20]. The method relies on the use of clear pots, allowing the visualization of roots that reach the transparent walls of the pot ("wall roots") after having grown buried in the soil. Importantly, in our method, the root system develops entirely inside the soil, in a three-dimensional, natural manner. This contrasts with other methods that rely on the use of rhizotrons, where the roots grow in a restricted, almost 2D soil environment that severely limits their radial expansion. The shallowness of each wall root (wall root shallowness, WRS) is measured, and the depth of the root system is expressed as the average of all single WRS measurements (mean WRS). Deep-rooted and shallow-rooted systems are characterized by relatively low or high mean WRS values, respectively.

The mean WRS parameter captures differences of root depth at a developmental stage defined by a minimum number of wall roots or WRS measurements set by the user (in our case, five; Arabidopsis and rice), and not at a specific time after planting. Importantly, estimates of root depth by the mean WRS parameter are independent of total root biomass, and the method does not measure root length distribution by depth, typical of, for example, shovelomics-based approaches. ClearDepth's second trait, resolution time (RT), considers the temporal aspect of root system expansion in the soil. It is defined as the time needed for a root system to display the minimum number of wall roots established by the user. Although less sensitive than the mean WRS parameter to differentiate between shallow and deep root systems, RT can be especially useful to compare root systems that do not differ significantly in mean WRS: the faster the wall roots appear in deep soil layers, the deeper the root system.

The value of ClearDepth is not limited to estimations of Arabidopsis' root depth; the method can also be applied to species characterized by larger, fibrous root systems, like the monocot rice. In addition, the method not only distinguishes between shallow-rooted and deep-rooted systems but is sensitive enough to detect small differences in root depth determined by the influence of, for example, an environmental factor like light. ClearDepth’s main limitation is the relatively long times (30–45 days) that some deep-rooted systems require to display wall roots. This and other aspects of the method, such as the automation of WRS measurements and the extension of its use to species other than Arabidopsis and rice, are current areas of improvement and exploration. In conclusion, ClearDepth offers a simple, low-cost, and easily scalable method for robust and sensitive measurements of root depth in soil-filled pots in controlled environment settings. Here, we provide a detailed protocol for its use.