Stefanie Engler

The paper uses imaging mass cytometry to analyze human pancreatic samples and identifies early β-cell dysfunction and immune changes in type 1 diabetes progression.

The paper develops a statistical framework to estimate tissue spatial segregation, enabling optimal design of multiplexed imaging experiments by determining the necessary fields of view to capture all cell types.

scROSHI identifies cell types without training data by using pre-existing gene sets and hierarchical relationships, outperforming methods in low-data and high-diversity scenarios.

SCIM matches single-cell measurements across technologies by learning a technology-invariant latent space and applying bipartite matching, achieving high accuracy in real-world datasets.

The paper introduces a bead-based compensation workflow for mass cytometry to correct channel crosstalk, improving data quality and panel development.