Gunsagar Singh Gulati

This paper identifies histone modification-associated nucleosome positions in cfDNA that correlate with tumor fraction and cancer state across multiple cancers.

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

The paper develops MHC Attention, a deep learning model, and combines it with high-throughput screening to identify novel HLA-E presented cancer peptides.

This study investigates how transcription factor subtypes in small cell lung cancer influence response and resistance to DLL3-targeted therapy, revealing subtype-specific mechanisms of resistance.

APEX infers cancer gene expression from plasma cfChIP-seq data, outperforming existing methods and enabling subtype classification and biomarker identification.

SNAP improves classification performance for cell-free chromatin data while maintaining data retention through optimized quality filters and modular design.

The paper infers enhancer activity from cfDNA fragmentation patterns and proposes a chromatin fiber model to explain these signatures.

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 uses single-cell RNA-sequencing to analyze cell type-specific responses to young or aged blood in heterochronic parabiosis, identifying mitochondrial function as a key factor in rejuvenation.

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 uses scRNA-seq to analyze microglia and myeloid cells across brain regions and developmental stages, revealing heterogeneity in postnatal microglia and their roles in disease.

On patterned flow cells using exclusion amplification, up to 5-10% of reads are credited to the wrong sample in a pool, traced to free index primers surviving library cleanup.