David A. Hafler

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.

The paper benchmarks generative methods for designing peptides in TCR-pMHC complexes, revealing method-dependent differences in backbone generation and conformational diversity.

The paper identifies increased myeloid-driven inflammation in the CNS of prodromal Parkinson's Disease patients, linking gut and CNS immune responses through shared T cell clones.

This study uses single-cell RNA sequencing to analyze immune changes after B-cell depletion in MS patients, revealing cell type-specific effects on macrophages, monocytes, and T cells.

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.

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

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 paper performs single-cell transcriptomic and proteomic analysis of Parkinson's disease brains, identifying cell-type-specific changes and differences from Alzheimer's disease.

This study identifies CD16/CD226low NK cells with inflammatory signatures and reduced effector functions in multiple myeloma patients, correlating with poor clinical outcomes.

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

Spatial-CITE-seq profiles proteins and transcriptomes in tissues, revealing spatially distinct immune responses in mouse and human tissues.

The paper shows enzymatic dissociation causes gene expression changes in microglia and proposes a protocol to reduce these artifacts in scRNA-seq.

ProtAnno automates cell type annotation for single cell proteomics data by integrating multiple reference sources, achieving accurate and robust results on manually labeled datasets.

NEBULA improves differential expression and co-expression analysis in large-scale multi-subject single-cell data by efficiently handling overdispersion with a negative binomial mixed model.

The paper improves genetic mapping resolution in autoimmune diseases by identifying shared alleles across multiple conditions and reveals widespread shared mechanisms.

The paper shows that the MS risk variant rs7665090G increases astrocyte NF-κB signaling and lymphocyte recruitment, leading to larger MS lesions.

The paper identifies a PTGER2-β-catenin axis that balances IFNγ and IL-10 in FoxP3+ Tregs, linking high salt environments to autoimmunity.