Ruth R. Montgomery

Severity-stratified, longitudinal cis-eQTL mapping enables high-sensitivity detection of candidate causal genes for critical COVID-19

This paper identifies 23 candidate causal genes for critical COVID-19 by integrating severity-stratified, longitudinal cis-eQTL mapping with GWAS signals from 1,031 patients.

Biologic therapy is associated with selective changes in airway eosinophil subpopulations in severe asthma

This paper shows biologic therapy in severe asthma reduces a specific activated eosinophil subpopulation linked to worse asthma control and lung function.

Identification of a multi-omics factor predictive of long COVID in the IMPACC study

The paper identifies a multi-omics recovery factor predictive of long COVID using data from the IMPACC study, showing it can predict LC risk early after infection.

Unraveling SARS-CoV-2 Host-Response Heterogeneity through Longitudinal Molecular Subtyping

This study identifies immune subtypes in hospitalized COVID-19 patients linked to clinical outcomes and long COVID risk through longitudinal multi-omics analysis.

Chronic Viral Reactivation and Associated Host Immune Response and Clinical Outcomes in Acute COVID-19 and Post-Acute Sequelae of COVID-19

This study investigates viral reactivation in COVID-19 patients, finding associations between reactivation of specific virus families and clinical outcomes, including mortality and PASC symptoms.

The Immune Signatures Data Resource: A compendium of systems vaccinology datasets

The paper introduces the Immune Signatures Data Resource, a compendium of standardized systems vaccinology datasets for comparative vaccine response analysis.

Single-cell longitudinal analysis of SARS-CoV-2 infection in human airway epithelium

Time-course single-cell RNA sequencing of infected human bronchial epithelial cultures reports ciliated cells as a major target, with tropism later widening to basal and club cells.

Exploring Single-Cell Data with Deep Multitasking Neural Networks

SAUCIE is a deep neural network that processes large single-cell datasets, performs batch correction, and identifies cluster-based signatures in dengue patient T cells.

Gating mass cytometry data by deep learning

The paper introduces DeepCyTOF, a deep learning method for automating cell gating in mass cytometry data, achieving high concordance with manual gating and performing well on benchmark challenges.