Jeremy A. Miller

The paper uses single-nucleus RNA sequencing and spatial transcriptomics to compare oligodendrocyte functions in gray and white matter across aging and Alzheimer's Disease.

This study uses single-nucleus RNA-seq, ATAC-seq, and Multiome to analyze Alzheimer's disease across multiple brain regions, revealing shared and region-specific cellular vulnerabilities.

This paper introduces a standards framework for integrating single-cell and spatial omics data into brain cell atlases, enabling interoperable and reusable knowledge-ready resources.

MapMyCells maps unlabeled single-cell datasets to reference brain taxonomies, enabling cross-study integration and reproducible cell-type annotation.

The paper develops a cross-species enhancer-AAV toolkit for targeting specific cell types in the basal ganglia across species, validated in mouse and macaque.

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 maps AD-related pathological changes in the caudate nucleus at cellular resolution, identifying distinct features and cell type-specific responses.

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.

This study identifies Apbb1ip as a genetic modulator of immune gene co-expression in aged mouse hippocampus, revealing age-related transcriptomic changes and immune response upregulation.

The paper evaluates computational methods for predicting cell type-specific enhancers in the mouse cortex using in vivo validation data and finds that open chromatin and sequence models improve prediction accuracy.

ACE compares cell type annotations across studies, enabling users to align and analyze cell type classifications and metadata in health and Alzheimer's Disease research.

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 introduces a data-driven method to extend cell ontologies for multi-modal single-cell omics data, focusing on the Primary Motor Cortex in humans, mice, and marmosets.

This study characterizes and simulates changes in human hippocampal granule cells during epilepsy progression, identifying ion channel modifications that increase excitability.

The paper provides a guide to the BRAIN Initiative Cell Census Network (BICCN) data ecosystem, its tools, and resources for analyzing brain cell types across species.

The paper assesses inter-individual variation in human cortical cell types across 75 individuals, finding significant variation in abundance and gene expression, particularly in deep layer glutamatergic neurons.

This study evaluates six algorithms for cell type matching in spatial transcriptomics data and presents consensus strategies for improved analysis.

This study compares cellular organization and interactions in human and mouse cortex using single-cell transcriptome imaging, finding conserved laminar organization but divergent cell-cell interactions in humans.

The paper compares cellular diversity in the primary motor cortex across human, marmoset, and mouse using single-nucleus transcriptomics and epigenomics, finding conserved cell types and species-specific adaptations.

NS-Forest identifies minimal marker gene combinations for cell type determination from scRNA-seq data, revealing key roles of signaling and non-coding RNAs in neuronal identity.

The paper spatially maps human cortical cell types using in situ sequencing and single-nucleus RNA-sequencing to create a first spatial atlas of human cortical cells.

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 identifies five major transcriptional patterns of brain disease risk genes across the adult human brain and highlights species-specific differences in psychiatric and substance abuse-related diseases.

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

ExSeq enables multiplexed RNA mapping at nanoscale resolution by expanding biological specimens for in situ sequencing.

FR-Match matches cell type clusters from scRNA-seq data using the Friedman-Rafsky test, improving accuracy and identifying novel cell phenotypes.

PRDD-seq simultaneously reconstructs cell type, lineage, and birthdate in human brain tissue by analyzing RNA and DNA mutations in single cells.

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