Louise A. Huuki-Myers

Multi-omic spatial mapping of the human habenula defines the molecular and anatomical organization of medial and lateral subregions

This study maps the spatiomolecular organization of the human habenula using multi-omic approaches, identifying molecular signatures and spatial domains linked to psychiatric disorders.

APOE E4 Alzheimer's Risk Converges on an Oligodendrocyte Subtype in the Human Entorhinal Cortex

The paper investigates how APOE genotype affects gene expression in the human entorhinal cortex, identifying an oligodendrocyte subtype as a potential mediator of Alzheimer's disease risk.

Transcriptomic analysis of the human habenula in schizophrenia

The paper performs transcriptomic analysis of the human habenula and identifies molecular changes associated with schizophrenia risk and illness state.

Impact of Alzheimer's disease risk factors and local neuromelanin content on the transcriptomic landscape of the human locus coeruleus

The paper analyzes the transcriptomic landscape of the human locus coeruleus in relation to Alzheimer's disease risk factors and neuromelanin content using spatially-resolved transcriptomics.

lute: estimating the cell composition of heterogeneous tissue with varying cell sizes using gene expression

lute improves cell composition estimation in bulk RNA-seq by accounting for varying cell sizes, using simulated and real datasets to demonstrate its accuracy.

Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex

The paper benchmarks cellular deconvolution methods using a multi-assay dataset from human prefrontal cortex, finding Bisque and hspe to be the most accurate.

Network nature of ligand-receptor interactions underlies disease comorbidity in the brain

The paper investigates ligand-receptor interactions (LRIs) in neurological diseases and finds that 96.8% of disease-associated LRIs are interconnected, highlighting their role in disease comorbidity and potential drug targets.

Massively parallel characterization of psychiatric disorder-associated and cell-type-specific regulatory elements in the developing human cortex

The paper identifies 46,802 enhancers and 164 psychiatric disorder variants with regulatory effects in the developing cortex and organoids using massively parallel reporter assays and deep learning.

Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex

The paper generates a molecular neuroanatomical map of the human prefrontal cortex using spatial transcriptomics and single nucleus RNA-sequencing, revealing novel spatial domains and cell-cell interactions linked to psychiatric disorders.

Data-driven Identification of Total RNA Expression Genes (TREGs) for Estimation of RNA Abundance in Heterogeneous Cell Types

The paper introduces a data-driven method to identify Total RNA Expression Genes (TREGs) for estimating RNA abundance in heterogeneous cell types and identifies AKT3 as a top TREG in brain regions.

Genetic and environmental contributions to ancestry differences in gene expression in the human brain

The paper examines how genetic ancestry influences gene expression and DNA methylation in admixed African/Black Americans, showing environmental and genetic factors affect brain disease risk.

escheR: Unified multi-dimensional visualizations with Gestalt principles

escheR improves 2D data visualization for multi-dimensional data using Gestalt principles, with applications in genomics and spatial transcriptomics.

Single-nucleus transcriptome analysis reveals cell type-specific molecular signatures across reward circuitry in the human brain

The paper uses snRNA-seq to analyze five human brain regions, identifies novel neuronal subpopulations, and links them to psychiatric and substance use risks.

SPEAQeasy: a Scalable Pipeline for Expression Analysis and Quantification for R/Bioconductor-powered RNA-seq analyses

SPEAQeasy provides a user-friendly, scalable RNA-seq pipeline that simplifies data processing and connects to R/Bioconductor tools for downstream analysis.