Details

Autor(en) / Beteiligte

• Bian, Benjamin
• Paquet, Agnès
• Arguel, Marie-Jeanne
• Meyer, Mickael
• Peyre, Ludovic
• Chalabi, Asma
• Péré, Marielle
• Lebrigand, Kevin
• Waldmann, Rainer
• Barbry, Pascal
• Hofman, Paul
• Roux, Jérémie

Titel

Coupling live-cell imaging and in situ isolation of the same single cell to profile the transient states of predicted drug-tolerant cells

Ist Teil von

• STAR protocols, 2022-09, Vol.3 (3), p.101600-101600, Article 101600

Ort / Verlag

Elsevier Inc

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Cell response variability is a starting point in cancer drug resistance that has been difficult to analyze because the tolerant cell states are short lived. Here, we present fate-seq, an approach to isolate single cells in their transient states of drug sensitivity or tolerance before profiling. The drug response is predicted in live cells, which are laser-captured by microdissection before any drug-induced change can alter their states. This framework enables the identification of the cell-state signatures causing differential cell decisions upon treatment. For complete details on the use and execution of this protocol, please refer to Meyer et al. (2020). [Display omitted] •Fate-seq provides gene sets of transient cell states•Fate-seq can profile the differential cell fates upon therapeutic treatment•It produces a molecular signature of cell sensitivity at a given drug dose Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Cell response variability is a starting point in cancer drug resistance that has been difficult to analyze because the tolerant cell states are short lived. Here, we present fate-seq, an approach to isolate single cells in their transient states of drug sensitivity or tolerance before profiling. The drug response is predicted in live cells, which are laser-captured by microdissection before any drug-induced change can alter their states. This framework enables the identification of the cell-state signatures causing differential cell decisions upon treatment.