Lisa Kim

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

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 identifies transcriptomic and morpho-electric diversity in human neocortical layer 1 interneurons, revealing greater subclass distinction compared to mouse.

The paper uses viral labeling and Patch-seq to define morpho-electric properties of GABAergic interneurons in human neocortex, revealing functional insights and reclassifying some SST neurons into PVALB subclass.

The paper presents a synaptic physiology dataset and analysis platform to study cortical microcircuit structure and synaptic properties in mouse and human neocortex, revealing layer-dependent connectivity and subclass-specific dynamics.

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 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.