Coupling live-cell imaging and in situ isolation of the same single cell to profile the transient states of predicted drug-tolerant cells
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...
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Veröffentlicht in: | STAR protocols 2022-09, Vol.3 (3), p.101600-101600, Article 101600 |
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Sprache: | eng |
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Zusammenfassung: | 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).
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•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. |
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ISSN: | 2666-1667 2666-1667 |
DOI: | 10.1016/j.xpro.2022.101600 |