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Single-cell transcriptomics has been employed in a growing number of animal studies, but the technique has yet to be widely used in plants. Nonetheless, early studies indicate that single-cell RNA-seq protocols developed for animal cells produce informative datasets in plants. We argue that single-cell transcriptomics has the potential to provide a new perspective on plant problems, such as the nature of the stem cells or initials, the plasticity of plant cells, and the extent of localized cellular responses to environmental inputs. Single-cell experimental outputs require different analytical approaches compared with pooled cell profiles and new tools tailored to single-cell assays are being developed. Here, we highlight promising new single-cell profiling approaches, their limitations as applied to plants, and their potential to address fundamental questions in plant biology.

Background Characterizing the behaviors of dynamic systems requires capturing them with high temporal and spatial resolution. Owing to its transparency and genetic tractability, the Arabidopsis thaliana root lends itself well to live imaging when combined with cell and tissue-specific fluorescent reporters. We developed a novel 4D imaging method that utilizes simple confocal microscopy and readily available components to track cell divisions in the root stem cell niche and surrounding region for up to 1 week. Results Using this method, we performed a direct measurement of cell division intervals within and around the root stem cell niche. The results reveal a short, steep gradient of cell division rates in proximal stem cells, with progressively more rapid cell division rates from quiescent center (QC), to cells in direct contact with the QC (initials), to their immediate daughters, after which division rates appear to become more homogeneous. Conclusions These results provide a baseline to study how perturbations in signaling could affect cell division patterns in the root meristem. This new setup further allows us to finely analyze meristematic cell division rates that lead to patterning.

As sessile organisms, root plasticity enables plants to forage for and acquire nutrients in a fluctuating underground environment. Here, we use genetic and genomic approaches in a \"split-root\" framework—in which physically isolated root systems of the same plant are challenged with different nitrogen (N) environments—to investigate how systemic signaling affects genome-wide reprogramming and root development. The integration of transcriptome and root phenotypes enables us to identify distinct mechanisms underlying \"N economy\" (i.e., N supply and demand) of plants as a system. Under nitrate-limited conditions, plant roots adopt an \"active-foraging strategy\", characterized by lateral root outgrowth and a shared pattern of transcriptome reprogramming, in response to either local or distal nitrate deprivation. By contrast, in nitrate-replete conditions, plant roots adopt a \"dormant strategy\", characterized by a repression of lateral root outgrowth and a shared pattern of transcriptome reprogramming, in response to either local or distal nitrate supply. Sentinel genes responding to systemic N signaling identified by genome-wide comparisons of heterogeneous vs. homogeneous split-root N treatments were used to probe systemic N responses in Arabidopsis mutants impaired in nitrate reduction and hormone synthesis and also in decapitated plants. This combined analysis identified genetically distinct systemic signaling underlying plant N economy: (i) N supply, corresponding to a long-distance systemic signaling triggered by nitrate sensing; and (ii) N demand, experimental support for the transitive closure of a previously inferred nitrate–cytokinin shoot–root relay system that reports the nitrate demand of the whole plant, promoting a compensatory root growth in nitrate-rich patches of heterogeneous soil.

The definition of cell identity is a central problem in biology. While single-cell RNA-seq provides a wealth of information regarding cell states, better methods are needed to map their identity, especially during developmental transitions. Here, we use repositories of cell type-specific transcriptomes to quantify identities from single-cell RNA-seq profiles, accurately classifying cells from Arabidopsis root tips and human glioblastoma tumors. We apply our approach to single cells captured from regenerating roots following tip excision. Our technique exposes a previously uncharacterized transient collapse of identity distant from the injury site, demonstrating the biological relevance of a quantitative cell identity index.

The organs of multicellular species consist of cell types that must function together to perform specific tasks. One critical organ function is responding to internal or external change. Some cell-specific responses to changes in environmental conditions are known, but the scale of cell-specific responses within an entire organ as it perceives an environmental flux has not been well characterized in plants or any other multicellular organism. Here, we use cellular profiling of five Arabidopsis root cell types in response to an influx of a critical resource, nitrogen, to uncover a vast and predominantly cell-specific response. We show that cell-specific profiling increases sensitivity several-fold, revealing highly localized regulation of transcripts that were largely hidden from previous global analyses. The cell-specific data revealed responses that suggested a coordinated developmental response in distinct cell types or tissues. One example is the cell-specific regulation of a transcriptional circuit that we showed mediates lateral root outgrowth in response to nitrogen via microRNA167, linking small RNAs to nitrogen responses. Together, these results reveal a previously cryptic component of cell-specific responses to nitrogen. Thus, the results make an important advance in our understanding of how multicellular organisms cope with environmental change at the cell level.

Time lapse microscopy is a transformative technique for plant cell and developmental biology. Light sheet microscopy, which manipulates the amount of light a sample is exposed to in order to minimize phototoxicity and maximize signal intensity, is an increasingly popular tool for time lapse imaging. However, many light sheet imaging systems are not designed with the unique properties of plant samples in mind. Recent advances have decreased the cost and increased the technical accessibility of light sheet microscopy, but plant samples still require special preparation to be compatible with these new systems. Here, we apply a novel light sheet microscopy system to regenerating Arabidopsis roots damaged via laser ablation. To adapt this system for Arabidopsis roots we establish a new protocol for sample mounting, as well as an automated root tip tracking system that requires no additional proprietary software. The methods presented here can be used to increase researcher access to long-term time-lapse imaging in Arabidopsis biology.

Background In the past few years, there has been an explosion in single-cell transcriptomics datasets, yet in vivo confirmation of these datasets is hampered in plants due to lack of robust validation methods. Likewise, modeling of plant development is hampered by paucity of spatial gene expression data. RNA fluorescence in situ hybridization (FISH) enables investigation of gene expression in the context of tissue type. Despite development of FISH methods for plants, easy and reliable whole mount FISH protocols have not yet been reported. Results We adapt a 3-day whole mount RNA-FISH method for plant species based on a combination of prior protocols that employs hybridization chain reaction (HCR), which amplifies the probe signal in an antibody-free manner. Our whole mount HCR RNA-FISH method shows expected spatial signals with low background for gene transcripts with known spatial expression patterns in Arabidopsis inflorescences and monocot roots. It allows simultaneous detection of three transcripts in 3D. We also show that HCR RNA-FISH can be combined with endogenous fluorescent protein detection and with our improved immunohistochemistry (IHC) protocol. Conclusions The whole mount HCR RNA-FISH and IHC methods allow easy investigation of 3D spatial gene expression patterns in entire plant tissues.

Plant development is remarkably plastic but how precisely can the plant customize its form to specific environments? When the plant adjusts its development to different environments, related traits can change in a coordinated fashion, such that two traits co-vary across many genotypes. Alternatively, traits can vary independently, such that a change in one trait has little predictive value for the change in a second trait. To characterize such \"tunability\" in developmental plasticity, we carried out a detailed phenotypic characterization of complex root traits among 96 accessions of the model Arabidopsis thaliana in two nitrogen environments. The results revealed a surprising level of independence in the control of traits to environment - a highly tunable form of plasticity. We mapped genetic architecture of plasticity using genome-wide association studies and further used gene expression analysis to narrow down gene candidates in mapped regions. Mutants in genes implicated by association and expression analysis showed precise defects in the predicted traits in the predicted environment, corroborating the independent control of plasticity traits. The overall results suggest that there is a pool of genetic variability in plants that controls traits in specific environments, with opportunity to tune crop plants to a given environment.

The current histoclinical breast cancer classification is simple but imprecise. Several molecular classifications of breast cancers based on expression profiling have been proposed as alternatives. However, their reliability and clinical utility have been repeatedly questioned, notably because most of them were derived from relatively small initial patient populations. We analyzed the transcriptomes of 537 breast tumors using three unsupervised classification methods. A core subset of 355 tumors was assigned to six clusters by all three methods. These six subgroups overlapped with previously defined molecular classes of breast cancer, but also showed important differences, notably the absence of an ERBB2 subgroup and the division of the large luminal ER+ group into four subgroups, two of them being highly proliferative. Of the six subgroups, four were ER+/PR+/AR+, one was ER−/PR−/AR+ and one was triple negative (AR−/ER−/PR−). ERBB2-amplified tumors were split between the ER−/PR−/AR+ subgroup and the highly proliferative ER+ LumC subgroup. Importantly, each of these six molecular subgroups showed specific copy-number alterations. Gene expression changes were correlated to specific signaling pathways. Each of these six subgroups showed very significant differences in tumor grade, metastatic sites, relapse-free survival or response to chemotherapy. All these findings were validated on large external datasets including more than 3000 tumors. Our data thus indicate that these six molecular subgroups represent well-defined clinico-biological entities of breast cancer. Their identification should facilitate the detection of novel prognostic factors or therapeutical targets in breast cancer.