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An increase in the frequency and intensity of heat waves, floods, droughts and other environmental stresses, resulting from climate change, is threatening agricultural food production worldwide. Heat waves are especially problematic to grain yields, as the reproductive processes of almost all our main grain crops are highly sensitive to heat. At times, heat waves can occur together with drought, high ozone levels, pathogen infection and/or waterlogging stress that suppress the overall process of plant cooling by transpiration. We recently reported that under conditions of heat and water-deficit stress combination, the stomata on sepals and pods of soybean (Glycine max) remain open, while the stomata on leaves close. This process, termed ‘differential transpiration’, enabled the cooling of reproductive organs, while leaf temperature increased owing to suppressed transpiration. In this review article, we focus on the impacts on crops of heat waves occurring in isolation and of heat waves combined with drought or waterlogging stress, address the main processes impacted in plants by these stresses and discuss ways to mitigate the negative effects of isolated heat waves and of heat waves that occur together with other stresses (i.e. stress combination), on crops, with a focus on the process of differential transpiration. This article is part of the theme issue ‘Crops under stress: can we mitigate the impacts of climate change on agriculture and launch the ‘Resilience Revolution’?’. 

Abstract As with phenotyping of any microscopic appendages, such as cilia or antennae, phenotyping of root hairs has been a challenge due to their complex intersecting arrangements in two-dimensional images and the technical limitations of automated measurements. Digital Imaging of Root Traits at Microscale (DIRT/μ) is a newly developed algorithm that addresses this issue by computationally resolving intersections and extracting individual root hairs from two-dimensional microscopy images. This solution enables automatic and precise trait measurements of individual root hairs. DIRT/μ rigorously defines a set of rules to resolve intersecting root hairs and minimizes a newly designed cost function to combinatorically identify each root hair in the microscopy image. As a result, DIRT/μ accurately measures traits such as root hair length distribution and root hair density, which are impractical for manual assessment. We tested DIRT/μ on three datasets to validate its performance and showcase potential applications. By measuring root hair traits in a fraction of the time manual methods require, DIRT/μ eliminates subjective biases from manual measurements. Automating individual root hair extraction accelerates phenotyping and quantifies trait variability within and among plants, creating new possibilities to characterize root hair function and their underlying genetics. 

Membrane proteins work in large complexes to perceive and transduce external signals and to trigger a cellular response leading to the adaptation of the cells to their environment. Biochemical assays have been extensively used to reveal the interaction between membrane proteins. However, such analyses do not reveal the unique and complex composition of the membrane proteins of the different plant cell types. Here, we conducted a comprehensive analysis of the expression of Arabidopsis membrane proteins in the different cell types composing the root. Specifically, we analyzed the expression of genes encoding membrane proteins interacting in large complexes. We found that the transcriptional profiles of membrane protein-encoding genes differ between Arabidopsis root cell types. This result suggests that different cell types are characterized by specific sets of plasma membrane proteins, which are likely a reflection of their unique biological functions and interactions. To further explore the complexity of the Arabidopsis root cell membrane proteomes, we conducted a co-expression analysis of genes encoding interacting membrane proteins. This study confirmed previously reported interactions between membrane proteins, suggesting that the co-expression of genes at the single cell-type level can be used to support protein network predictions. 

Abstract Single-cell and single-nucleus RNA-sequencing technologies capture the expression of plant genes at an unprecedented resolution. Therefore, these technologies are gaining traction in plant molecular and developmental biology for elucidating the transcriptional changes across cell types in a specific tissue or organ, upon treatments, in response to biotic and abiotic stresses, or between genotypes. Despite the rapidly accelerating use of these technologies, collective and standardized experimental and analytical procedures to support the acquisition of high-quality data sets are still missing. In this commentary, we discuss common challenges associated with the use of single-cell transcriptomics in plants and propose general guidelines to improve reproducibility, quality, comparability, and interpretation and to make the data readily available to the community in this fast-developing field of research.