Leeat Keren

This study develops a bispecific antibody that enhances T-cell engagement with AXL+ cells, improving anti-PD-1 therapy efficacy in HNSCC by remodeling the tumor microenvironment.

CellTune is a software tool for cell classification in spatial proteomics that outperforms existing methods with high accuracy and resolution.

The paper investigates the relationship between cell shape, protein expression, and tissue state using spatial imaging and machine learning, finding a bidirectional link and identifying p53-positive tumor cells.

This paper introduces a multiplexed imaging technique for lung cancer diagnosis that improves accuracy and efficiency by analyzing multiple biomarkers from a single tissue section.

The paper creates a spatial atlas of human gastrointestinal GVHD, identifying immune and epithelial changes that contribute to disease pathophysiology.

The paper introduces CISM, a method to identify spatial multicellular motifs associated with disease states in single-cell spatial data, showing they outperform existing methods in predicting disease states in TNBC and melanoma.

The paper uses spatial proteomics and transcriptomics to analyze sentinel lymph nodes in melanoma patients, identifying immune patterns that predict distant metastases.

This paper introduces DeepCell Types, a language-informed vision model for spatial proteomics cell phenotyping that generalizes across diverse datasets and marker panels.

This paper introduces CombPlex, a combinatorial staining method combined with deep learning to measure more proteins at single-cell resolution without needing specialized imaging equipment.

CellSighter is a deep-learning pipeline that classifies cells in multiplexed images with high accuracy, reducing manual effort and improving consistency.

The paper introduces Mesmer, a deep learning algorithm for cell segmentation in tissue images, achieving human-level performance and enabling automated feature extraction.

The paper constructs a spatiotemporal atlas of the maternal-fetal interface using multiomic data to show how gestational age influences immune and stromal cell interactions and spiral artery remodeling.

The paper investigates the trade-off between precision and economy in gene expression rates and finds that certain rate combinations are missing due to selective pressures.