Sean C. Bendall

This study identifies APOE4/4-driven dysfunctional microglial states in Alzheimer's disease through multiomic analysis, revealing shifts toward terminal states with metabolic disruption and inflammatory signaling.

This study investigates how cell cycle dynamics influence T cell fate and exhaustion by analyzing single-cell data and manipulating cell cycle progression and signaling.

The paper identifies diverse human antibody-secreting cell subsets using multi-omic methods and shows their distinct functional and spatial characteristics.

This study analyzes multi-omic data from gliomas to identify therapeutic escape mechanisms and TME evolution, finding that N-glycosylation classifies tumor grade and immune transcriptome predicts GBM survival.

This paper develops a deep single-cell mass cytometry approach to capture both canonical and noncanonical cell cycle states across diverse cell systems.

The paper introduces Nimbus, a deep learning model for predicting marker positivity in multiplexed imaging data using a pre-trained dataset, and shows its integration with clustering for cell subtype identification.

CellFuse integrates single-cell and spatial proteomics data using a deep learning framework, outperforming existing methods in accuracy and efficiency.

This study identifies IKAROS as a factor associated with antigen escape in B-ALL, showing that low IKAROS levels lead to reduced B-cell antigen expression and resistance to CD19- and CD22-targeted therapies.

This study identifies uridine metabolism as a therapeutic target for chemo-resistant B-ALL by linking active signaling to uridine dependency and demonstrating DHODH inhibition as a promising treatment.

The paper analyzes single exosomes in CSF from Alzheimer's and Parkinson's patients and controls, finding reduced ApoE+ exosomes in Alzheimer's patients.

This study uses spatial proteomics to analyze human brain tissues and identifies proteopathies and cell interactions in Alzheimer's disease.

This paper introduces a supervised dimensionality reduction method using hybrid subset selection with linear discriminant analysis to improve visualization and analysis of single-cell data.

The paper introduces InTAC-seq to quantify chromatin accessibility and transcription factor abundance in fixed cells, showing GATA1's role in erythroid lineage commitment.

The paper analyzes synaptic molecular changes in Alzheimer's disease and related conditions using mass cytometry and imaging, identifying disease-specific molecular differences.

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 introduces a machine learning method that integrates immunological knowledge to improve predictive power from mass cytometry data.

The paper maps EMT and MET states in lung cancer using single-cell resolution and a novel computational tool, showing differences in trajectories and providing a framework for clinical sample characterization.