Michelle L. Curtis

This paper introduces VIMA, a method for analyzing spatial molecular data to identify disease-associated spatial features, showing improved performance over existing methods in simulations and real datasets.

The paper identifies an age-related expansion of an inflammatory and quiescent HSC subpopulation using integrated single-cell data.

The paper introduces TCAT, a pipeline to annotate T-cell GEPs, identifying 46 reproducible programs across diverse tissues and diseases.

A single-cell atlas of blood and tissue samples from lupus nephritis patients and healthy controls identifies stromal and immune populations, including scar-associated macrophages, which correlate with disease activity.

The paper benchmarks snATAC-seq integration pipelines using multimodal datasets and recommends SnapATAC2 + Harmony with ENCODE cCRE features for effective integration.

This study identifies granzyme K as a driver of a novel complement activation pathway independent of classical, lectin, or alternative pathways.

The paper develops SCENT, a method to create cell-type-specific enhancer-gene maps from single-cell multimodal data, identifying causal variants and genes for diseases.

The paper uses CRISPR editing and multimodal single-cell sequencing to identify the functional effects of disease variants in primary human cells.

The paper maps cell-type-specific eQTLs for HLA genes across multiple tissues and identifies dynamic regulation of HLA-DQ genes in immune cells.

This paper provides a statistical genetics guide for imputing HLA alleles from genotype data and offers best practices for association testing to identify HLA variants driving complex diseases.

The paper deconstructs RA synovium using single-cell data to identify six cell type abundance phenotypes associated with distinct inflammatory states.