Kyle J. Travaglini

This paper introduces scDMGC, a method to analyze high-dimensional transcriptomic data using topological data analysis, revealing cell-type organization and transcriptional gradients in brain datasets.

This study shows that SORL1 deficiency in neurons alters synaptic protein interactions, causes hyperexcitability, and impairs synaptic plasticity, linking endosomal recycling defects to Alzheimer's disease pathogenesis.

The paper presents a benchmarking challenge to predict Alzheimer's disease neuropathology from single-nucleus transcriptomics using human and AI models, finding that top methods achieve near-perfect categorical staging predictions.

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.

scVIP models single-cell transcriptomes to predict phenotypes and infer trajectories in development and disease.

This paper introduces a Bayesian framework to infer continuous disease states from discrete pathological protein measurements in Alzheimer's Disease, using pseudotime to model progression.

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

This study identifies sex-specific gene expression associations with Alzheimer's disease neuropathology and cognitive decline using single-nucleus RNA sequencing from human brain tissue.

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.

This study creates an integrated multimodal cell atlas of Alzheimer's disease by analyzing single nuclei transcriptomes, epigenomes, and spatial coordinates across AD stages.

The paper processes single-nuclei multi-omics data from 388 human brains to identify regulatory elements and build cell-type networks, revealing changes in aging and neuropsychiatric disorders.

The paper identifies somatic driver mutations in microglia of Alzheimer's disease brains, showing increased clonal expansion and inflammatory signatures.

The paper uses single nucleus RNA-sequencing to analyze human cortical areas, revealing cell type proportions and gene expression patterns that correlate with functional connectivity and areal specialization.

This study uses single nucleus transcriptomics to compare human and non-human primate cortical tissues, identifying human-specific gene expression patterns and their potential functional roles.

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.

Nearly 500,000 cells from 24 human tissues, many from one donor, characterize over 400 cell types and enable within-donor study of T-cell clones, B-cell mutation rates and splicing.

This paper identifies activated interstitial macrophages as a key target of SARS-CoV-2 infection in human lung tissue, showing their role in viral takeover, inflammation, and fibrosis.

The paper reports a direct budding mechanism of alveolar development distinct from the classical model of subdivision of large air sacs.

The paper creates a comprehensive cell atlas of mouse lemurs using single-cell RNA sequencing, identifying over 750 cell types and comparing their molecular profiles to humans, mice, and macaques.

This paper introduces Kmermaid and Orpheum to enable scRNA-seq analysis in species without reference genomes by detecting cell type-specific sequences without gene annotations.

The paper creates a comprehensive molecular cell atlas of the human lung using single-cell RNA sequencing, identifying 58 cell populations including 14 new types and revealing evolutionary changes in lung cell types.

Across 1,176,683 airway and lung cells from 22 datasets, ACE2 rises with age, in males and in smokers' multiciliated cells, and is lowest in young children.

On patterned flow cells using exclusion amplification, up to 5-10% of reads are credited to the wrong sample in a pool, traced to free index primers surviving library cleanup.