Kenneth J. Livak

The paper identifies recipient stem-cell persistence and T-cell dysregulation as predictors of relapse after HSCT in AML/MDS by analyzing single-cell RNA and TCR sequencing data.

This study identifies ASXL1 truncating mutations as a driver of leukemic resistance to T cell attack and proposes EZH2 inhibition as a therapeutic strategy to overcome this resistance.

The paper analyzes single-cell data from bone marrow samples to identify immune cell interactions that predict response to adoptive cellular therapy in myeloid leukemia.

This paper introduces IN-DEPTH and SGCC for spatial multi-omics analysis, revealing EBV-related immunomodulatory differences in DLBCL.

DIISCO infers dynamic intercellular interactions from time-series single-cell data using a Bayesian framework, showing its ability to capture time-dependent communication patterns in simulated and CAR-T cell data.

The paper shows that reduced Kv8.1 expression in ALS motor neurons with SOD1(A4V) and C9orf72 mutations increases vulnerability to cell death by affecting metabolism and transport pathways.

The paper introduces a novel single cell strand-specific total RNA sequencing method that overcomes limitations of existing techniques and detects more genes, including non-polyadenylated ones.

The paper analyzes MYOD-mediated reprogramming of human fibroblasts using single-cell RNA-Seq to identify molecular determinants of reprogramming success and failure.

This paper describes a method using DSP fixation to preserve single cells for RNA sequencing, showing it maintains RNA complexity and reduces technical variability.

The paper uses pseudotemporal ordering to analyze single-cell data and reveals dynamics and regulators of cell fate decisions.