MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data

Summary

This protocol describes how to use the MEDUSA analytical method to quantify the death regulatory effect of each gene knockout. It includes instructions on determining experimental conditions that optimize sensitivity and a step-by-step tutorial on performing the analysis.

Abstract

Systematic screening of gain- or loss-of-function genetic perturbations can be used to characterize the genetic dependencies and mechanisms of regulation for essentially any cellular process of interest. These experiments typically involve profiling from a pool of single gene perturbations and how each genetic perturbation affects the relative cell fitness. When applied in the context of drug efficacy studies, often called chemo-genetic profiling, these methods should be effective at identifying drug mechanisms of action. Unfortunately, fitness-based chemo-genetic profiling studies are ineffective at identifying all components of a drug response. For instance, these studies generally fail to identify which genes regulate drug-induced cell death. Several issues contribute to obscuring death regulation in fitness-based screens, including the confounding effects of proliferation rate variation, variation in the drug-induced coordination between growth and death, and, in some cases, the inability to separate DNA from live and dead cells. MEDUSA is an analytical method for identifying death-regulatory genes in conventional chemo-genetic profiling data. It works by using computational simulations to estimate the growth and death rates that created an observed fitness profile rather than scoring fitness itself. Effective use of the method depends on optimal tittering of experimental conditions, including the drug dose, starting population size, and length of the assay. This manuscript will describe how to set up a chemo-genetic profiling study for MEDUSA-based analysis, and we will demonstrate how to use the method to quantify death rates in chemo-genetic profiling data.

Introduction

In chemo-genetic profiling, systematic genetic perturbations are used to understand the contribution of each gene to a given drug response^1,2,3. These experiments are valuable and can reveal important insights about drug responses, including the drug's binding target and mechanisms of drug influx/efflux. However, because these experiments are typically performed in a pooled manner that evaluates all genes simultaneously, generating mechanistic insights from chemo-genetic profiling data can be challenging.

The control mechanisms and genetic depe....

Protocol

1. Optimization of the drug dose to induce cell death

NOTE: The protocol below is for optimizing one combination of cell line, drug, and dose using the FLICK assay^7,8. The example data for this protocol describes the optimization and screening of 5 μM etoposide in U2OS cells, but the same optimization steps could be applied to any other desired combination of cell line and drug/dose. This protocol does not require the collection or analysis of dead cells. This protocol can be used for suspension cultures, provided that dead cells are removed. For suspension cultu....

Discussion

In this protocol, we describe the use of the MEDUSA analytical method for quantifying chemo-genetic profiling data to score how genetic perturbations alter drug-induced growth and death rates. Because the primary output of a chemo-genetic profiling experiment is related to population size, not rates, the underlying rates are inferred using simulations. Thus, the critical steps in the method are the accurate experimental parameterization of drug response kinetics, which includes the untreated cell proliferation rate, the .......

Materials

Name	Company	Catalog Number	Comments
100mm Fisherbrand TC Dishes	Fisher Scientific	FB012924	For growing cells and optimizing population size
150mm Fisherbrand TC Dishes	Fisher Scientific	FB012925	For growing cells and optimizing population size
DESeq2	R Package	v1.44.0	For calculating fold-change
DMEM	Corning	10017CV	For seeding and drugging cells
DMSO	Fisher Scientific	MT-25950CQC	For seeding and drugging cells
Fisherbrand 96-Well, Cell Culture-Treated, U-Shaped-Bottom Microplate	Fisher Scientific	FB012932	For seeding and drugging cells (pin plate)
Greiner Bio-One CELLSTAR μClear 96-well, Cell Culture-Treated, Flat-Bottom Microplate	Greiner	655090	For seeding and drugging cells
MATLAB	Mathworks	R2024a	For performing FLICK, GRADE, and MEDUSA
Microplate fluorescence reader	Tecan	Spark	For dead cell measurements
Sytox Green	Thermo Fisher Scientific	S7020	For dead cell measurements
TKOV3 CRISPR library	Addgene	125517	For performing CRISPR screen
Triton-X 100	Thermo Fisher Scientific	J66624-AP	For permeabilizing cells
Trypan blue	Corning	MT25900CI	For measure live/dead cells