Agata Budzillo

The paper integrates transcriptomic, morphological, and electrophysiological data to define neuron types in mouse visual cortex and predict their projection targets.

This study uses multimodal Patch-seq data to show that gene expression gradients in mouse basal ganglia neurons correlate with spatial location, physiology, and morphology.

The paper analyzes multi-modal Patch-seq data from macaque striatum to reveal morphoelectric diversity and functional specialization of neuronal cell types, highlighting primate-specific features and differences from rodents.

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 creates a cross-species consensus atlas of primate basal ganglia cell types using multiomic data from human, macaque, and marmoset, identifying 61 conserved cell types and a novel hybrid medium spiny neuron type.

The paper describes an optimized Patch-seq technique for high-quality electrophysiological, morphological, and transcriptomic characterization of neurons, along with software tools for data acquisition and analysis.

The paper develops coupled autoencoders to identify cortical neurons across modalities and finds consistent cell types in transcriptomic and electrophysiological data.

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