HSP90-buffered genetic variation is common in Arabidopsis thaliana

HSP90 is a protein chaperone particularly important in the maturation of a diverse set of proteins that regulate key steps in a multitude of biological processes. Alterations in HSP90 function produce altered phenotypes at low penetrance in natural populations. Previous work has shown that at least some of these phenotypes are due to genetic variation that remains phenotypically cryptic until it is revealed by the impairment of HSP90 function. Exposure of such “buffered” genetic polymorphisms can also be accomplished by environmental stress, linking the appearance of new phenotypes to defects in protein homeostasis. Should such polymorphisms be widespread, natural selection may be more effective at producing phenotypic change in suboptimal environments. In evaluating this hypothesis, a key unknown factor is the frequency with which HSP90-buffered polymorphisms occur in natural populations. Here, we present Arabidopsis thaliana populations suitable for genetic mapping that have constitutively reduced HSP90 levels. We employ quantitative genetic techniques to examine the HSP90-dependent polymorphisms affecting a host of plastic plant life-history traits. Our results demonstrate that HSP90-dependent natural variation is present at high frequencies in A. thaliana, with an expectation that at least one HSP90-dependent polymorphism will affect nearly every quantitative trait in progeny of two different wild lines. Hence, HSP90 is likely to occupy a central position in the translation of genotypic variation into phenotypic differences

Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain

The human brain has enormously complex cellular diversity and connectivities fundamental to our neural functions, yet difficulties in interrogating individual neurons has impeded understanding of the underlying transcriptional landscape. We developed a scalable approach to sequence and quantify RNA molecules in isolated neuronal nuclei from post-mortem brain, generating 3,227 sets of single neuron data from six distinct regions of the cerebral cortex. Using an iterative clustering and classification approach, we identified 16 neuronal subtypes that were further annotated on the basis of known markers and cortical cytoarchitecture. These data demonstrate a robust and scalable method for identifying and categorizing single nuclear transcriptomes, revealing shared genes sufficient to distinguish novel and orthologous neuronal subtypes as well as regional identity within the human brain

Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis

The transcriptional state of a cell reflects a variety of biological factors, from persistent cell-type specific features to transient processes such as cell cycle. Depending on biological context, all such aspects of transcriptional heterogeneity may be of interest, but detecting them from noisy single-cell RNA-seq data remains challenging. We developed PAGODA to resolve multiple, potentially overlapping aspects of transcriptional heterogeneity by testing gene sets for coordinated variability amongst measured cells