Patrick S. Stumpf

The paper identifies TNF-driven transcriptional programs in Langerhans cells that regulate immunogenic and tolerogenic responses through IRF1 and other transcription factors.

This paper uses transfer learning to map biology from mouse to human bone marrow using single-cell RNA-sequencing data, showing improved accuracy with retraining and zero-shot learning.

The paper identifies that Langerhans cells (LCs) upregulate a tolerogenic gene module during migration, which is critical for inducing regulatory T cells.

The paper uses machine learning to identify regulatory network archetypes in single-cell data, revealing distinct cell states in mouse embryonic stem cells.

This study uses single-cell profiling and deep learning to show that hematopoietic stem cell self-renewal declines with age due to stochastic division patterns and prior division history.

This study shows that stem cell differentiation involves stochastic transitions through hidden microstates, revealing epigenetic memory and a statistical mechanics framework for cellular identity.