Protocol for Comprehensive Synthetic Lethality Screens

Damia Romero-Moya1, Jeroen P Roose1,2,3

  • 1Department of Anatomy, University of California San Francisco (UCSF), 513 Parnassus Avenue, Room HSW-1326, San Francisco, CA 94143-0452, USA.

STAR Protocols
|September 28, 2020
PubMed

Insights

This study details a protocol for synthetic lethality screening in leukemia cells, using a PI3K inhibitor and gene depletion to identify cell death triggers. The method is adaptable for researchers investigating cancer therapeutics.

Area of Science:

  • Cell Biology
  • Molecular Biology
  • Cancer Research

Background:

  • Synthetic lethality is a promising strategy for targeted cancer therapy.
  • Identifying synthetic lethal interactions requires robust screening methods.
  • Jurkat T cell leukemia provides a model for studying T cell malignancies.

Purpose of the Study:

  • To provide a detailed protocol for synthetic lethality screening in Jurkat T cells.
  • To establish a method for measuring the combinatorial effects of PI3K inhibition and gene depletion.
  • To facilitate the adaptation of this protocol by other investigators.

Main Methods:

  • Utilized an ultra-complex shRNA library for comprehensive gene depletion.
  • Employed cell death as the primary readout for synthetic lethality.
  • Combined a pan-PI3K inhibitor (GDC0941) with specific gene depletion.
  • Detailed protocol steps, coverage considerations, time frames, and potential bottlenecks.

Main Results:

  • Successfully established a protocol for synthetic lethality screening in a leukemia cell line.
  • Demonstrated the measurement of combinatorial drug and gene-knockdown effects.
  • Provided practical insights and troubleshooting for protocol execution.

Conclusions:

  • The described protocol is readily adaptable for researchers in cancer and molecular biology.
  • This methodology aids in discovering novel synthetic lethal interactions for therapeutic development.
  • The protocol facilitates efficient screening of drug-gene interactions in leukemia models.