ABSTRACT
Improving nutrient and water uptake in crops is one of the major challenges to sustain a fast-growing population that faces increasingly nutrient-limited soils. Root hairs, which are specialized epidermal cells, are important drivers of nutrient and water uptake from the soil. Microscopy provides a mean to record root hairs as digital images. However, due to their geometry and complex spatial arrangements quantifying root hairs in microscopy images manually remains a bottleneck. Manual selection of representative root hairs can result in inaccurate estimations of root hair traits and misrepresentation of root hair functions. We present a method to quantify phenotypes automatically by measuring all individual root hairs in digital microscopy images. Our method uses random forests classification to separate root hair from the parent root and the image background. We define metrics to evaluate segments of root hairs that intersect or form blobs of two or more root hairs. Using simulated annealing for combinatorial optimization, we reconstruct individual root hairs by resolving intersections in a globally optimal way. As a result, we measure the root hair length, its distribution, and root hair density in each image. We demonstrate our method on examples of three maize cultivars under phosphorus, nitrogen, and potassium stress. Results show that our measurements of root hair traits strongly correlate with manually measured data in mean root hair length (R 2: 0.72 to 0.85,p <.001), as well as in root hair density (R 2: 0.38 to 0.66, p <.001). We show that our method computes reliable estimates of root hair length, density and their distributions along the root on complex root hair arrangements in maize. We believe that our study paves a way towards identifying the genetic control of root hair traits and increased agricultural production.
Keywords: root hair, abiotic stress, phenotyping, machine learning, simulated annealing, trait distribution
INTRODUCTION
Branching patterns in biology occur at all spatial scales, organismal levels and for many physiological, protective or reproductive reasons and are most prominent in the plant kingdom [1]. Hair-like structures with a high length to width ratio are specific objects that branch off and extend from the organism’s surface and have diverse functions across biology [2]. To study their function it is necessary to quantify their shape and arrangement accurately, which, despite of their simple shape—in comparison to a multi-level branching architecture—remains a challenge. Modern imaging tools can capture digital images of these hair-like structures, but extracting all of them individually from the image is ambiguous if they occlude each other partially. The occlusion is especially prevalent in root hairs, which are elongated epidermal cells extending from the root surface of a plant. By increasing the root surface area and extending away from the root surface into the soil, root hairs can increase water and nutrient uptake from the soil [3].
We demonstrate an algorithm to resolve occlusion in 2D microscopy images of root hairs. Researcher studying morphological traits of root hairs traditionally use microscopy images and quantify root hair density and length manually. Not only is this manual trait measurement of root hair traits extremely tedious but the interpretation of 2D images representing root hairs is also subjective. If measurements are done automatically, only the total area or a profile of root hair length along the root can be extracted [4, 5]. Other studies used X-ray computed tomography (CT) to scan roots and root hairs in soil, but still used manual tracing to segment root hairs from 3D X-ray CT scans [6-8].
The challenge to extract individual root hairs from microscopy images is that all intersections of root hairs must be resolved in the 2D projection of the image. A single intersection of two root hairs can be instantly resolved by determining the straightest solution from a small number of possible combinations. By increasing the number of root hairs and intersections, however, the number of potential combinations increases in real scenarios to trillions of possible outcomes. We present an approach to strategically resolve intersections and extract individual root hairs in feasible computational time. As such, our approach allows to measure root hair traits, like length and density, and their distributions within a root sample at a much finer resolution than previous 2D approaches.
MATERIAL AND METHODS