Summary Host defense to RNA viruses depends on rapid intracellular recognition of viral RNA by two cytoplasmic RNA helicases, RIG-I and MDA5. RNA transfection experiments indicate that RIG-I responds to naked double-stranded (ds) RNAs with a triphosphorylated 5′ (5′ppp) terminus. However, identity of the RIG-I stimulating viral structures in an authentic infection context remains unresolved. We show that incoming viral nucleocapsids containing a 5′ppp dsRNA “panhandle” structure trigger antiviral signaling that commences with RIG-I, is mediated through the adaptor protein MAVS, and terminates with transcription factor IRF-3. Independent of mammalian cofactors or viral polymerase activity, RIG-I bound to viral nucleocapsids, underwent a conformational switch, and homo-oligomerized. Enzymatic probing and super-resolution microscopy suggest that RIG-I interacts with the panhandle structure of the viral nucleocapsids. These results define cytoplasmic entry of nucleocapsids as the proximal RIG-I-sensitive step during infection, and establish viral nucleocapsids with a 5′ppp dsRNA panhandle as a RIG-I activator.

Single-cell level measurements are necessary to characterize the intrinsic biological variability in a population of cells. In this study, we demonstrate that, with the microarrays for mass spectrometry platform, we are able to observe this variability. We monitor environmentally (2-deoxy-D-glucose) and genetically (ΔPFK2) perturbed Saccharomyces cerevisiae cells at the single-cell, few-cell, and population levels. Correlation plots between metabolites from the glycolytic pathway, as well as with the observed ATP/ADP ratio as a measure of cellular energy charge, give biological insight that is not accessible from population-level metabolomic data.single-cell measurements | MALDI mass spectrometry | baker's yeast E ven genetically identical cells present in the same microenvironment can express different phenotypes, for a number of reasons: cell-to-cell heterogeneity can stem from differences in the cell age and differences in the cell cycle stage, and stochastic effects together with feedback mechanisms can lead to distinctively different phenotypes, too (1-6). As population-level measurement techniques inherently average out such cell-to-cell differences, biochemical mechanisms underlying a studied system cannot be deduced from such measurements. Thus, to detect and exploit this heterogeneity, new analytical platforms with a sensitivity at the single-cell level and the ability to perform quantitative analyses must be developed and validated.Motivated by advances of mass spectrometry (MS) in metabolomics, the analytical chemistry community has stepped up its efforts toward realizing MS-based single-cell metabolomics (1, 2). A number of analytical approaches were developed with detection limits low enough for single-cell metabolite analyses [e.g., nanostructured surfaces (7, 8), postionization techniques (9, 10), modified laser optics (11), the use of microsampling tools (12, 13), microarrays for MS measurements (14, 15), etc.]. Until now, however, most MS studies targeting single-cell metabolite analysis have only shown the analytical capabilities, but have not demonstrated that true biological information can be retrieved from studying the metabolism of single cells.Here, using the unicellular eukaryotic model organism Saccharomyces cerevisiae, we present an analytical validation of a single-cell metabolite analysis using the microarrays for mass spectrometry (MAMS) platform. This validation concerns both the analytical methodology and the biological information, by monitoring expected cellular responses upon an environmental and a genetic perturbation. Furthermore, we present examples of biological insight that are only accessible with a platform such as MAMS. Specifically, we unravel metabolite-metabolite correlations, and visualize coexisting subpopulations in an isogenic cell culture. This technology can now be used to reveal metabolic differences in cells of isogenic cell populations, such as differences caused by cell cycles stages, cell ages, or stochastically induced phenotypic differences. Results and ...

Symbioses between plants and mycorrhizal fungi are ubiquitous in ecosystems and strengthen the plants' defense against aboveground herbivores. Here, we studied the underlying regulatory networks and biochemical mechanisms in leaves induced by ectomycorrhizae that modify herbivore interactions. Feeding damage and oviposition by the widespread poplar leaf beetle were reduced on the ectomycorrhizal hybrid poplar × Integration of transcriptomics, metabolomics, and volatile emission patterns via mass difference networks demonstrated changes in nitrogen allocation in the leaves of mycorrhizal poplars, down-regulation of phenolic pathways, and up-regulation of defensive systems, including protease inhibitors, chitinases, and aldoxime biosynthesis. Ectomycorrhizae had a systemic influence on jasmonate-related signaling transcripts. Our results suggest that ectomycorrhizae prime wounding responses and shift resources from constitutive phenol-based to specialized protective compounds. Consequently, symbiosis with ectomycorrhizal fungi enabled poplars to respond to leaf beetle feeding with a more effective arsenal of defense mechanisms compared with nonmycorrhizal poplars, thus demonstrating the importance of belowground plant-microbe associations in mitigating aboveground biotic stress.

Isotopic labelling of cellular metabolites, used in conjunction with high-density micro-arrays for mass spectrometry enables observation of ATP metabolism in single yeast cells.

Studying cell-to-cell heterogeneity requires techniques which robustly deliver reproducible results with single-cell sensitivity. Through a new fabrication method for the microarrays for mass spectrometry (MAMS) platform, we now have attained robustness and reproducibility in our single-cell level mass spectrometry measurements that allowed us to combine single-cell MAMS-based measurements from different days and samples. By combining multiple measurements, we were able to identify three co-existing phenotypes in an isogenic population of Saccharomyces cerevisiae characterized by distinctively different levels of glycolytic intermediates.