Eliza R. Barkan

The paper integrates multimodal data to characterize human neocortical glutamatergic neurons, revealing morphoelectric properties that distinguish transcriptomic subclasses and highlight species-specific differences.

The paper uses single-cell RNA-seq to study signaling pathway dependencies in zebrafish pectoral fin development, identifying new cell types and signaling relationships.

This paper introduces two tools to infer genetic requirements from single-cell sequencing data of perturbed embryos, revealing new roles for transcription factors in zebrafish development.

This paper creates a high-resolution transcriptomic and spatial cell type atlas of the whole adult mouse brain, revealing spatial organization and transcription factor codes for cell types.

The paper uses single-cell RNA-seq to study temperature sensitivity in zebrafish embryos, finding that notochord sheath cells are particularly vulnerable due to protein misfolding.

The paper uses single nucleus RNA-sequencing to analyze human cortical areas, revealing cell type proportions and gene expression patterns that correlate with functional connectivity and areal specialization.

The paper uses single-cell RNA-seq to analyze relay neurons in the dLGN across species, finding subtle transcriptomic differences compared to morphological and functional distinctions.

The paper characterizes transcriptomic and physiological properties of GABAergic cortical neurons in mice, identifying 20 interneuron met-types with consistent features across modalities.

The paper identifies transcriptomic diversity in human supragranular neurons and links it to functional specialization and disease vulnerability.

The paper identifies von Economo neurons as a regionally specialized subtype of extratelencephalic-projecting excitatory neurons using single nucleus RNA-sequencing and cross-species comparisons.

The paper compares cell types in human and mouse cortex using single nucleus RNA-sequencing, finding conserved architecture with species-specific differences.

The paper classifies neurons in mouse visual cortex into 17 electrophysiological and 35 morphological types using unsupervised methods and links them to transcriptomic types.

The paper shows that single-nucleus and single-cell RNA-seq can equally identify neuronal cell types in mouse visual cortex when intronic sequences are included.

The paper identifies shared and distinct transcriptomic cell types in two mouse neocortical regions using scRNA-seq, showing most excitatory types are region-specific while inhibitory types are shared.