Mikko M. Turunen

The paper identifies treatment-resistant Cancer Stem-Like Cells in breast cancer using single-cell RNA sequencing and validates a drug combination to target them effectively.

The paper uses large-scale perturbation profiles to analyze drug mechanisms of action and polypharmacology across multiple tumor subtypes and tissues.

The paper uses phosphoproteomic time-series data and the VESPA algorithm to analyze drug resistance mechanisms in colon cancer cells, revealing adaptive signaling rewiring confirmed by CRISPRko assays.

The paper uses CRISPRi and single-cell RNA-seq to reverse engineer gene regulatory networks in colorectal cancer cells, revealing autoregulatory and cooperative mechanisms.

This paper introduces RSLs as UMIs to improve CRISPR/Cas9 screening reproducibility by enhancing precision and reducing required cells and sequencing reads.