Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization.

The Patch-seq approach is a powerful variation of the patch-clamp technique that allows for the combined electrophysiological, morphological, and transcriptomic characterization of individual neurons. To generate Patch-seq datasets at scale, we identified and refined key factors that contribute to the efficient collection of high-quality data. We developed patch-clamp electrophysiology software with analysis functions specifically designed to automate acquisition with online quality control. We recognized the importance of extracting the nucleus for transcriptomic success and maximizing membrane integrity during nucleus extraction for morphology success. The protocol is generalizable to different species and brain regions, as demonstrated by capturing multimodal data from human and macaque brain slices. The protocol, analysis and acquisition software are compiled at https://githubcom/AllenInstitute/patchseqtools. This resource can be used by individual labs to generate data across diverse mammalian species and that is compatible with large publicly available Patch-seq datasets.

Conserved cell types with divergent features in human versus mouse cortex.

Elucidating the cellular architecture of the human cerebral cortex is central to understanding our cognitive abilities and susceptibility to disease. Here we used single-nucleus RNA-sequencing analysis to perform a comprehensive study of cell types in the middle temporal gyrus of human cortex. We identified a highly diverse set of excitatory and inhibitory neuron types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to similar mouse cortex single-cell RNA-sequencing datasets revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of properties of human cell types. Despite this general conservation, we also found extensive differences between homologous human and mouse cell types, including marked alterations in proportions, laminar distributions, gene expression and morphology. These species-specific features emphasize the importance of directly studying human brain.

Transcriptional landscape of the prenatal human brain.

The anatomical and functional architecture of the human brain is mainly determined by prenatal transcriptional processes. We describe an anatomically comprehensive atlas of the mid-gestational human brain, including de novo reference atlases, in situ hybridization, ultra-high-resolution magnetic resonance imaging (MRI) and microarray analysis on highly discrete laser-microdissected brain regions. In developing cerebral cortex, transcriptional differences are found between different proliferative and post-mitotic layers, wherein laminar signatures reflect cellular composition and developmental processes. Cytoarchitectural differences between human and mouse have molecular correlates, including species differences in gene expression in subplate, although surprisingly we find minimal differences between the inner and outer subventricular zones even though the outer zone is expanded in humans. Both germinal and post-mitotic cortical layers exhibit fronto-temporal gradients, with particular enrichment in the frontal lobe. Finally, many neurodevelopmental disorder and human-evolution-related genes show patterned expression, potentially underlying unique features of human cortical formation. These data provide a rich, freely-accessible resource for understanding human brain development.

Gene expression analyses in maize inbreds and hybrids with varying levels of heterosis.

BACKGROUND: Heterosis is the superior performance of F1 hybrid progeny relative to the parental phenotypes. Maize exhibits heterosis for a wide range of traits, however the magnitude of heterosis is highly variable depending on the choice of parents and the trait(s) measured. We have used expression profiling to determine whether the level, or types, of non-additive gene expression vary in maize hybrids with different levels of genetic diversity or heterosis. RESULTS: We observed that the distributions of better parent heterosis among a series of 25 maize hybrids generally do not exhibit significant correlations between different traits. Expression profiling analyses for six of these hybrids, chosen to represent diversity in genotypes and heterosis responses, revealed a correlation between genetic diversity and transcriptional variation. The majority of differentially expressed genes in each of the six different hybrids exhibited additive expression patterns, and approximately 25% exhibited statistically significant non-additive expression profiles. Among the non-additive profiles, approximately 80% exhibited hybrid expression levels between the parental levels, approximately 20% exhibited hybrid expression levels at the parental levels and ~1% exhibited hybrid levels outside the parental range. CONCLUSION: We have found that maize inbred genetic diversity is correlated with transcriptional variation. However, sampling of seedling tissues indicated that the frequencies of additive and non-additive expression patterns are very similar across a range of hybrid lines. These findings suggest that heterosis is probably not a consequence of higher levels of additive or non-additive expression, but may be related to transcriptional variation between parents. The lack of correlation between better parent heterosis levels for different traits suggests that transcriptional diversity at specific sets of genes may influence heterosis for different traits.