Karthik Shekhar

The paper reconstructs the evolutionary origins of retinal amacrine cell diversity using transcriptomic data from 24 vertebrate species, identifying 42 orthologous cell types and suggesting a hybrid precursor origin.

This paper demonstrates that ensembles of voltage-gated ion channels can undergo bioelectrical phase transitions driven by feedback between channel currents and membrane voltage.

The paper shows that the L2/3 continuum in the mouse visual cortex develops through two phases: a genetically programmed initial phase and a visually experience-driven second phase.

The paper uses single-cell RNA sequencing to analyze the transcriptomic impact of disrupted cholinergic retinal waves in RGCs, finding modest type-specific gene expression changes.

The paper constructs a single-nucleus transcriptomic atlas of the mouse whisker somatosensory cortex during development and finds that sensory experience minimally affects cell type maturation in this region, unlike the visual cortex.

The paper uses transcriptomic data from six vertebrate species to investigate the evolutionary origin of the tetrapod double cone, finding that both members originated from ancestral red cones and rods emerged before cone diversification.

The paper uses spatial transcriptomics and computational methods to show that vision affects both cell state and cell type composition in the visual cortex during critical periods.

The paper shows that the ChAHP complex regulates upper-layer neuron production and NDD risk genes during neocortical development through progenitor proliferation and gene regulation.

The paper maps the spatial distribution of retinal ganglion cell types and identifies perivascular RGCs that show neuroprotection after optic nerve injury.

The paper compares retinal cell composition and visual coding between diurnal and nocturnal mice to show how evolutionary changes in cell types reconfigure visual information processing.

The paper identifies two rod bipolar cell types in zebrafish that are related to mammalian rod bipolar cells, suggesting the primary rod pathway evolved over 400 million years ago.

The paper integrates single-cell transcriptomic data from 17 vertebrate species to study the evolution of retinal cell types, finding conserved cell classes and subclasses with variation increasing from outer to inner retina.

The paper creates a comprehensive human retina cell atlas using single-cell multi-omics data, revealing over 110 cell types and their gene regulatory networks, and enables fine-mapping of GWAS and eQTL variants.

This study uses scRNA-seq to analyze molecular maturation of mouse retinal ganglion cells, showing visual activity is not required for type identity but is needed for full maturation.

This study uses single-cell RNA-seq to show that mouse retinal ganglion cell precursors diversify into discrete types through gradual fate restriction after mitosis.

The paper shows that vision selectively drives the specification of upper-layer glutamatergic cell types in the mouse visual cortex through gene expression regulation.

The paper classifies mouse retinal ganglion cells into 42 types using functional, morphological, and transcriptomic data and provides an online database for classification.

The paper classifies zebrafish retinal ganglion cells using single-cell transcriptomics, identifies marker genes, and links molecular profiles to functional behaviors.

The paper uses scRNA-seq to analyze 85,000 retinal cells from humans, identifies 58 cell types, and compares gene expression between fovea, peripheral retina, and macaques to study blinding diseases.

The paper identifies neuroprotective genes in retinal ganglion cells by analyzing their resilience to injury using single-cell RNA-seq and functional assays.

The paper classifies and compares cell types in primate fovea and peripheral retina using single-cell RNA-seq, finding conserved and divergent features across species and regions.

This study shows that T helper cells interact with intestinal stem cells via MHCII to regulate their renewal and differentiation, with distinct effects based on Th subtype and cytokine signaling.

DroNc-Seq enables high-throughput single nucleus RNA-seq analysis of archived brain tissues, demonstrating efficient and unbiased cell type classification in mouse and human samples.