Aaron M. Newman

This study identifies BCL11B as a determinant of chemoresistance in breast cancer, showing it promotes a persister state that TNFα can reverse.

The paper identifies and isolates human AML leukemia stem cells using single-cell multi-omics and establishes a prognostic gene signature for clinical relevance.

The paper constructs a pan-cancer single-cell atlas to improve tumor identity inference and identify genetic vulnerabilities by integrating high-quality malignant cells across multiple cancers.

This study uses single-cell and spatial transcriptomics to identify distinct tumor states in uterine leiomyosarcomas and proposes drug targets based on these states.

The paper introduces CytoTRACE 2, a deep learning framework to assess cell potency from scRNA-seq data, showing improved performance across multiple datasets and applications.

This study identifies new functionally and molecularly distinct multipotent progenitors in adult human hematopoiesis using multiomic single cell analyses and functional assays.

The paper evaluates deconvolution methods for inferring immune infiltration from bulk gene expression using a DREAM Challenge, finding that some community-contributed methods, especially deep learning-based ones, perform well.

CytoSPACE aligns single-cell and spatial transcriptomes using convex linear optimization, outperforming existing methods in noise-tolerance, accuracy, and efficiency.

The paper uses integrated spatial multi-omics to study fibroblast fate during tissue repair and defines mechanisms controlling cell fate in wound healing.

The paper introduces CytoTRACE, a method using transcriptional diversity to predict differentiation states from scRNA-seq data, outperforming existing methods and identifying stem cells and tumor-related genes.

This study identifies CDK19 as a therapeutic target for TNBC by showing its role in tumor growth and metastasis, with minimal impact on normal cells.