William Pilcher

Integrated coding-noncoding genome annotation expands single-cell transcriptomic discovery and identifies clinically relevant noncoding RNAs in multiple myeloma

The paper integrates coding and noncoding genome annotations to improve transcriptomic analysis, identifying clinically relevant noncoding RNAs in multiple myeloma.

Spatial multi-omics of multiple myeloma uncovers niche-dependent pro-myeloma and immunosuppressive signaling in the bone marrow and extramedullary lesions

This study uses spatial and single-cell transcriptomics to identify niche-specific signaling programs in multiple myeloma that drive progression and immunosuppression.

A single-cell atlas characterizes dysregulation of the bone marrow immune microenvironment associated with outcomes in multiple myeloma

The paper characterizes immune microenvironment dysregulation in multiple myeloma using single-cell data and links it to disease progression and patient outcomes.

Single-cell analysis of pediatric acute myeloid leukemia samples uncovers treatment-resistant stem and mast cells

This study uses single-cell RNA sequencing to identify treatment-resistant subtypes and mast cell-like cells in pediatric AML, linking them to poor outcomes.

Longitudinal multi-omic profiling uncovers immune escape and predictors of response in multiple myeloma

This study uses longitudinal multi-omic profiling to identify immune escape mechanisms and predictors of response in multiple myeloma.

Integrated single-nuclei and spatial transcriptomic analysis reveals propagation of early acute vein harvest and distension injury signaling pathways following arterial implantation

The paper uses single-nuclei and spatial transcriptomics to analyze vein graft injury pathways, identifying key signaling networks involved in graft failure after harvest and implantation.

Single Cell Transcriptomic Landscape of Diabetic Foot Ulcers

The paper profiles single-cell RNA sequencing data from diabetic foot ulcers to identify cell types and gene expression patterns associated with wound healing.

Shape-to-graph Mapping Method for Efficient Characterization and Classification of Complex Geometries in Biological Images

The paper introduces a shape-to-graph mapping method for quantifying and classifying complex geometries in biological images using graph-based metrics and machine learning.