Name: medusa for identifying death regulatory genes in chemo-genetic profiling data
Uploaded: 2025-02-07T14:45:00.000+00:00
Duration: 7 min 17 s
Description: Systematic screening of gain- or loss-of-function genetic perturbations can be used to characterize the genetic dependencies and mechanisms of regulation ...

We thank all members of the Lee Lab, past and present, for their contributions to our lab&#39;s point of view on evaluating drug responses. This work was supported by funding MJL and MEH from the National Institutes of Health (U01CA265709, R21CA294000, and R35GM152194 to MJL, and F31CA268847 to MEH), the funding MJL from the Jayne Koskinas Ted Giovanis Foundation.

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 assay7,8. The example data for this protocol describes the optimization and screening of 5 &#956;M 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 cultures, dead cells can be sorted/separated using a viability marker or a marker specific for cell death.
<ol>
	<li>Before performing a FLICK assay, optimize cell number, Triton-X permeabilization, SYTOX concentration, and plate reader acquisition settings using the detailed protocol described in8.</li>
	<li>Plate cells in black, clear-bottom 96-well plates. Typical seeding densities are 1500-5000 cells per well, and this number should be optimized per cell line. Incubate cells at 37 &#176;C with 5% CO2 and humidity for 16-24 h.</li>
	<li>Prepare drug dilutions in media containing the concentration of SYTOX that was identified as optimal in step (1.1). Drugs are typically tested across a log dilution series containing 8-12 biologically or clinically relevant doses. 
	NOTE: Certain drugs can emit fluorescence at the same wavelength as SYTOX, making quantification with this assay impossible. In such cases, utilizing SYTOX variants that emit fluorescence at different wavelengths might be beneficial. Additionally, this assay cannot assess the reaction to drugs that affect SYTOX fluorescence or prevent SYTOX from binding to DNA.</li>
	<li>Lyse one plate (T0) using Triton-X at the concentration optimized in step (1.1). After cell lysis, use a plate reader to measure the SYTOX fluorescence and determine the total starting cell number based on the empirically established relationship between fluorescence and cell number (see FLICK protocol7,8).</li>
	<li>Measure SYTOX fluorescence in the experimental plates at T0 using a plate reader, with settings optimized in step (1.1).</li>
	<li>Monitor the SYTOX signal over time for three days. To constrain the resulting kinetic data, use at least three time points per day, each 4 h apart. 
	NOTE: This protocol evaluates cell death and growth over 3 days. This length of time is usually sufficient for death-inducing drugs; however, the assay can be shortened or lengthened to accommodate drugs with alternate death kinetics.</li>
	<li>After the final timepoint, lyse the experimental plate(s) with Triton-X to determine the final population size of each condition.</li>
	<li>Calculate Fractional Viability (FV) and fit endpoint data to dose curves, as described in the FLICK protocol7,8.</li>
	<li>Identify drug doses that induce roughly 50% cell death by evaluating the FV dose curves. 
	NOTE: The identification of dead cells by SYTOX necessitates both plasma membrane rupture and nuclear envelope breakdown. These membranes need continuous upkeep, so SYTOX will identify dead cells regardless of the cell death mechanism induced by the drug. However, these processes do not happen with the same effectiveness for all forms of cell death, which could influence SYTOX&#39;s capacity to measure dead cells precisely. Additionally, different cell death mechanisms will produce cell corpses of varying stabilities. However, we have found that all forms of cell death tested so far result in SYTOX-bound DNA (either inside a permeabilized nucleus or in the cell culture media). This SYTOX signal is generally stable throughout a 72 h assay but can be monitored by evaluating the total cell number at the beginning and end of the assay.</li>
</ol>
2. Selection of assay length
<ol>
	<li>Using the FLICK data collected in step 1, calculate each condition&#39;s Lethal Fraction (LF) over time as described in the FLICK protocol7,8. Fit kinetic LF data to a lag-exponential death (LED) model, as described previously9.</li>
	<li>Plot LF over time for each lethal dose identified in step 1. Select a dose and time point that produces ~50% LF. 
	NOTE: A lethal fraction of ~50% enriches strong, death-specific signals in CRISPR screening data while leaving enough live cells behind to maintain library representation5. However, some drugs will be constrained by literature precedence or a narrow range of doses that induce a specific desired phenotype. If the dose is restricted, the assay endpoint should be shortened or extended to ensure the desired dose reaches ~50% LF. If 50% lethality is not possible, the size of the starting population may need to be increased.</li>
</ol>
3. Determining the starting population size
<ol>
	<li>Select the desired drug dose to further optimize screening based on the above optimization.</li>
	<li>Plate cells on 10 cm dishes in complete media. Each condition should be plated with technical triplicates and enough biological replicates so that cells can be counted every 12-24 h for the length of the screen, including the T = 0 time point. 
	NOTE: Plated cell numbers may need to be optimized separately for untreated and treated cells, considering net population growth rate and cell size. Cells should generally be plated at densities that result in a confluency that does not compromise cell health at the assay endpoint (i.e., for many cell types &#60; 80% confluent). Treated and untreated cells can also be replated during the screen, if necessary, as long as library representation is maintained.</li>
	<li>Incubate cells overnight at 37 &#176;C with 5% CO2.</li>
	<li>Add the drug at the desired concentration to the treatment plates. In parallel, harvest cells from 3 untreated plates and count the number of live cells using a hemocytometer. Stain the dead cells with trypan blue (or a comparable viability dye) to ensure that only live cells are counted. These data establish the number of cells at the T = 0 time point.</li>
	<li>After 12-24 h, harvest cells from 3 untreated and 3 treated plates and count the number of live cells. Repeat the harvesting until the assay endpoint is reached. To fully understand the drug and cell death kinetics, it is recommended that live cells be monitored for 24-48 hours following the time point identified in step (2.2).</li>
	<li>Visualize changes in viability by plotting population size (y-axis) over time (x-axis). 
	NOTE: Based on the maximum observed cell number and the final population size, a rough estimate of the lethal fraction at the endpoint can be made. This should be compared to the fractional viability and lethal fraction data generated using the FLICK assay (step 1.9 and step 2.2) to ensure that the expected amount of cell death is achieved.</li>
	<li>Identify the time point with the smallest population size. Depending on the amount of growth that occurs in the presence of the drug, this will be at either the start or end of the CRISPR screen.</li>
	<li>Select a starting population size. Ensure the starting number of cells is optimized to maintain a minimum of 500x-1000x library coverage throughout the screen. 
	NOTE: For example, the TKOv3 library has 70,948 sgRNAs. To represent this library at 500x coverage, no fewer than 35,474,000 cells per replicate per condition should be maintained throughout the assay. This bottleneck population size is often encountered at the beginning of the assay or following trypsinization and downsampling for untreated or proliferating conditions. In contrast, the bottleneck population size is often dictated by the population size at the assay endpoint for drug treatments that induce substantial lethality.</li>
	<li>If desired, repeat the optimization above on the plates that will be used for the CRISPR screen (e.g., 15 cm plates) to confirm that drug efficacy scales correctly.</li>
</ol>
4. Performing CRISPR screen
NOTE: Cells can be screened using established CRISPR screening methods following optimization of drug dose and assay length. Established protocols, such as the TKO screening protocol from the Moffat lab10,11, can be used to prepare the CRISPR screening library, produce viruses, perform the CRISPR screen, and prepare libraries for sequencing (Figure 1A-B). The protocol below describes steps specific to the MEDUSA analysis, which should be carried out on count-level data following sequencing.
<ol>
	<li>Calculate experimentally observed fold changes for each sgRNA (Figure 1C) as described below.
	<ol>
		<li>Generate a sgRNA-level counts table to prepare sequencing data for analysis. Trim and map sequencing libraries using standard pipelines, as described5,12.</li>
		<li>Normalize sequencing library depth. Normalize libraries either manually or using built-in functions such as those in DESeq2, as in 5,12. Perform normalization against all guides or by using the distribution of non-targeting guides.</li>
		<li>Randomly assign non-targeting sgRNAs to non-targeting genes. For example, a library with four sgRNAs per gene should have four non-targeting sgRNAs per non-targeting gene.</li>
		<li>For each comparison of interest (untreated/T0 and/or treated/untreated), calculate the log2(fold-change). It is recommended that this be calculated using a parametric fit in DESeq2, although a manual calculation of the fold change could also be performed.&#160;</li>
		<li>Trim sgRNAs with a low number of counts. The correct trimming strategy will depend on the level of noise between replicates and how this varies over counts. These features will vary depending on the CRISPR library, and test multiple cutoffs in parallel for new data. Common cut-off strategies based on literature precedent include removing the lowest 5% of the guides or removing guides below some nominal threshold13.</li>
	</ol>
	</li>
	<li>Determine the impact of each single gene knockout on the growth rate as described below.
	<ol>
		<li>Calculate the net population growth rate (NPG) of the untreated cells (i.e., the observed population doubling time, Figure 1D). This can be extracted from the FLICK data in step 1, live cell counts over time in step 3, or prior experimental measurements for the cell line of interest.</li>
		<li>MEDUSA requires three parameters to model the untreated condition: NPG: Net Population Growth rate, in doublings/h; Tstart: Starting time point, in h. This is set to a default of 0; Tendunt: Final time point for the untreated condition, in h.</li>
		<li>Simulate all possible perturbations to the NPG rate. Use a for loop to evaluate a range of possible NPG values. For each relative growth rate, calculate the log2(fold-change) that would be observed at the assay endpoint.</li>
		<li>For each experimental sgRNA in step 4.1.5, identify the simulated fold-change closest to the observed data. Assign the relative growth rate that resulted in the simulated fold-change to that sgRNA.</li>
	</ol>
	</li>
	<li>Characterize the coordination between growth and death as described below. 
	NOTE: The parameters required for MEDUSA can be extracted from FLICK-style data when analyzed with the GRADE analysis method14. Data from (1) and (2) can be used for this, or an additional parameterization experiment can be performed.
	<ol>
		<li>MEDUSA uses four parameters to characterize the coordination between growth and death in the presence of drug: GRdrug1: Drug-induced growth rate before death onset, in doublings/h; DO: Death onset time, in h; GRdrug2: Drug-induced growth rate after death onset, in doublings/h; DRdrug: Drug-induced death rate, in units of Lethal Fraction/h. To parameterize DO, use LF kinetics. Extract the death onset time from the LED fit.</li>
	</ol>
	</li>
	<li>Determine DRdrug using LF kinetics. Determine the average death rate by dividing the final LF by the time following death onset (in h).</li>
	<li>Determine GRdrug1 from the live cell counting data in step 3. Before death onset, determine the drug-induced growth rate by fitting the data to a simple exponential equation.
	<ol>
		<li>Determine GRdrug2 using a combination of data from step 3 and drug GRADE14. Calculate and plot the GRADE for the selected drug dose on a GRADE plot. If GRADE = 0, assume GRdrug2 to be 0; If GRADE &#62; 0, use GRADE to determine the population&#39;s average growth rate. Based on the experimentally determined value for GRdrug1, determine the value for GRdrug2 that results in the observed average growth rate. 
		NOTE: The average drug-induced growth rate can be inferred from a GRADE plot by computing the pairwise distance between the observed data point (i.e., pair of GR and FV data for a drug) and each point that makes up the GRADE space and identifying the closest point. For detailed instructions, see ref14. Drug-induced growth rates and death rates are not fixed values per drug and are specific to a drug at a given dose in a given cell context.</li>
	</ol>
	</li>
	<li>Generate simulated fold-changes for all possible combinations of growth rate and death rate as described below.
	<ol>
		<li>Using the experimentally determined coordination of drug-induced growth rate and death rate, build a model that simulates the drug-induced population size over time (Figure 1D). This model can be built as a piece-wise function which combines two independent phases of the drug response (pre- and post-death onset time).</li>
		<li>For a full MEDUSA model, include these eight parameters: NPG: Net Population Growth rate, in doublings/h; GRdrug1: Drug-induced growth rate before death onset, in doublings/h; DO: Death onset time, in h; GRdrug2: Drug-induced growth rate after death onset, in doublings/h; DRdrug: Drug-induced death rate, in units of Lethal Fraction/h; Tstart: Starting time point, in h. This is set to a default of 0; Tendunt: Final time point for the untreated condition, in h; Tendtr: Final time point for the treated condition, in h.</li>
		<li>For the baseline model, simulate all possible growth and death rate perturbations. For each combination, calculate the log2(fold-change) that would be observed. 
		NOTE: In MEDUSA, perturbations to the growth rate are assumed to be proportional across NPG, GRdrug1, and GRdrug2. For example, an 80% reduction in the NPG rate also results in an 80% reduction in both drug-induced growth rates.</li>
	</ol>
	</li>
	<li>Infer the death rate of each knockout (Figure 1D-E) as described below.
	<ol>
		<li>For each sgRNA, triangulate the drug-induced death rate that would have produced the observed fold change. First, extract the relative growth rate determined in step (4.2.4).</li>
		<li>Determine rates for GRdrug1 and GRdrug2. Apply the relative growth rate from NPG to calculate a proportional growth rate for GRdrug1/GRdrug2.</li>
		<li>Using the constraints to NPG, GRdrug1, and GRdrug2, identify the simulated fold-change closest to the observed fold change for each sgRNA (treated/untreated). Use a combination of these observations to back-calculate the drug-induced death rate.</li>
	</ol>
	</li>
	<li>Calculate gene-level growth rates and death rates as described below.
	<ol>
		<li>Determine gene-level rates for each gene in the library. Calculate the mean for each growth rate and death rate for all sgRNAs associated with a given gene (typically 4-10).</li>
		<li>To calculate empiric p-values, bootstrap the sgRNA-level data. Perform an FDR correction with the Benjamini-Hochberg procedure. 
		NOTE: Complementary instructions, as well as sample data for analyzing CRISPR screening data using MEDUSA, are included on our GitHub repository (https://github.com/MJLee-Lab/MEDUSA).&#160;</li>
	</ol>
	</li>
</ol>

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The authors have no conflicts of interest to disclose.

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 drug-induced death onset time, the drug-induced death rate, and the drug GRADE-inferred coordination between growth and death rates. These features collectively generate the rules that enable mapping the observed populations to the underlying kinetic rates.
Although drug-specific parameterization is critical to the accuracy of MEDUSA&#39;s inferences, this analytical method could, in theory, still be used without drug-specific information. For instance, while it is likely that the drug dose, assay length, growth rate, and level of cell death can be quantified without any specialized machinery or expertise, quantifying the coordination between growth and death may be challenging for some labs. In these cases, users may prefer to run MEDUSA without explicitly characterizing this feature. In practice, this would amount to encoding a spectrum of plausible coordination values, resulting in a spectrum of different models and MEDUSA-inferred growth and death rates. Probabilistic estimates of the impact of gene perturbations on drug-induced cell death could likely be made for most genes.
A clear limitation of the method is that inferring the death regulatory function of a given gene requires that a drug is tested at a dose causing relatively high levels of cell death. In this protocol, we suggest a dose that results in ~ 50% lethal fraction (half of the cells are dead), which enables capturing both sensitizing and de-sensitizing effects. If/when drugs are tested at very low, non-lethal doses, growth rate variation can be sufficient to describe the observed population size without increasing/changing the death date. In these situations, MEDUSA will fail to identify a single pair of growth and death rates, as many possible pairs could create the same observed population size.
Aside from the aforementioned limitations, the method is likely to be the best, and in some cases the only, method for performing chemo-genetic profiling experiments in a manner that quantifies the impact of genetic perturbations on drug-induced cell death. Alternative methods, such as FACS sorting of dead cells, do exist, and these can be effective if sufficiently large populations of dead cells can be recovered for sequence-based analysis20. Collection of dead cells will be challenging if the rate of death is low, such that few dead cells accumulate at any given moment in time or in the context of some forms of necrotic death, where cells rupture and cannot be recovered. Given that the method does not require the collection or sequencing of dead cells, this method can be used to evaluate all types of cell death.

In chemo-genetic profiling, systematic genetic perturbations are used to understand the contribution of each gene to a given drug response1,2,3. These experiments are valuable and can reveal important insights about drug responses, including the drug&#39;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 dependencies for drug-induced cell death tend to be challenging to resolve in chemo-genetic profiling data. There are several reasons for this problem, but many of these stem from the impacts of variation in cell proliferation4. For instance, because cells proliferate exponentially, a genetic perturbation&#39;s effect on the proliferation rate has a larger impact on the population size than changing cell death rates. Furthermore, because this biased sensitivity is exacerbated over time and because these experiments are typically performed over several weeks, most studies are optimized to be highly sensitive to proliferation defects and essentially insensitive to changes in drug-induced cell death. Other proliferation-related issues include varied coordination between proliferation and death (e.g., how fast is each clone growing while also dying, and does this vary between genetic perturbations) and the variations in proliferation rates for each clone in the absence of drug, which changes the expected number of cells that should/could have been recovered if the drug was not effective. The bottom line is that chemo-genetic profiling experiments generally score the impact of genetic perturbations using measurements that are proportional to the relative population sizes, comparing treated and untreated populations. Because population size is a product of both the cell growth and cell death rates, from the perspective of cell death, proliferation represents a confounding influence.
To remedy these issues, we created a Method for Evaluating Death Using a Simulation-assisted Approach (MEDUSA)5. MEDUSA works by interpreting the observed relative population size data through the lens of computational simulations to infer the combination of drug-induced growth and death rates that generated the observed drug response for each genetic clone. Prior data suggest that the method can accurately infer how genetic perturbations affect the drug-induced death rate, but the accuracy of this method depends on a detailed understanding of how cell proliferation and cell death are coordinated by a drug and how these rates vary over time5,6. Additionally, MEDUSA-based inferences require a drug being tested at a dose that causes substantial cell death. Importantly, these drugging conditions create additional concerns about the starting population sizes and assay lengths, which should be carefully considered and optimized. In this protocol, we describe how to set up a chemo-genetic profiling study for MEDUSA-based analysis and provide a detailed use of this analytical method. The overall goal of MEDUSA is to determine how each gene deletion impacts drug-induced growth and death rates.

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

medusa for identifying death regulatory genes in chemo-genetic profiling data

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.

Using this protocol, we explored the genetic dependencies of etoposide-induced death in U2OS cells. Etoposide is a commonly used DNA-damaging chemotherapy15. This chemo-genetic profiling experiment was performed using the GeCKOv2 library16 in Cas9 expressing U2OS cells5. In these types of experiments, gene function is typically inferred from the relative abundance of a given knockout clone when comparing drug-treated versus untreated populations (Figure 1A-B). However, one cannot directly compare the counts for knockout cells across treated and untreated conditions, as the total treated and untreated populations are likely to be very different sizes. Thus, common analysis methods generally normalize the knockout cell counts to internal control, such as the cells containing non-targeting sgRNAs17,18 (i.e., WT cells in Figure 1C). Thus, the core analysis used in chemo-genetic profiling - generally some measure related to the log-scaled fold change for the normalized counts in treated versus untreated populations (L2FC) - represents the relationship between 4 dynamic populations, each of which is growing and dying at a different rate (Figure 1C). The purpose of the analysis is to infer the death rate for the drug-treated knockout population from a combination of the observed L2FC data and simulations of the population dynamics for each of the 4 populations in question (Figure 1D-E).
To identify an appropriate dose, we used the FLICK assay to score the lethality of etoposide in U2OS cells. At 72 h (a standard end time point), high levels of lethality were restricted to doses that are uncommonly used (&#62; 10 &#181;M, for example, data not shown). So, we extended the assay to 96 h. At this time point, 5 &#181;M etoposide caused ~ 40% - 50% lethality (Figure 2A). To establish an assay length and starting population size, we needed to evaluate how many cells could be recovered over time. Notably, although the FLICK data already revealed that the population should be 50% alive and 50% dead at 4 days, it was possible to choose a longer or shorter assay length based on the dynamics observed in the recovered population (how fast was the population shrinking?). Additionally, performing this cell counting in a large plating format after trypsinization and cell recovery confirmed that the recovered population had shrunk, suggesting that dead cells were adequately washed/removed from the population. These data confirmed that 4-day exposure to 5 &#181;M etoposide resulted in a recovered cell number that was 40%-50% smaller than the largest population size observed during the drug treatment time course (Figure 2B). Based on these data, we selected an initial population of 1 x 108 cells per treatment group.
Following the execution of the chemo-genetic profiling experiment, we used the drug GRADE analysis method to establish the coordination between growth and death following 5 &#181;M etoposide treatment in U2OS cells. Based on the projection of these data, it was calculated that 5 &#181;M etoposide resulted in a dramatically reduced growth rate, with cells dying only following complete growth arrest (Figure 2C). Thus, the underlying simulations of population dynamics for etoposide-treated cells encoded a GRdrug2 = 0 (Figure 2D). GRdrug1 was the average GR computed from GRADE analysis, and the death onset time (DO) was the death onset time observed in FLICK.
From the simulations, we computed the relationship between L2FC and the underlying growth rates and death rates (Figure 2E). To constrain each knockout to a single pair of growth rates and death rates, select the drug-induced growth rate, a growth rate that was proportional to the knockout&#39;s net population growth rate (i.e., NPGKO x GRdrug1). The result of this method was an explicit inference for the effect of each knockout on the growth rate and death rate of cells exposed to etoposide (Figure 2F).
To validate MEDUSA&#39;s inference, we selected a gene that produced conflicting predictions when evaluated using conventional methods versus when evaluated using MEDUSA. For instance, XRCC5 is a DNA repair factor19. A conventional analysis suggests that XRCC5 knockout creates modest etoposide resistance (XRCC5 is placed in the red area in Figure 2F, suggesting that it is more abundant in the treated condition than in the untreated condition). This would be surprising, given that the knockout of XRCC5 should compromise the cell&#39;s ability to fix DNA damage, likely causing cells to experience a higher level of etoposide-induced damage. Importantly, however, MEDUSA reveals that XRCC5 abundance accumulates in the treatment population due to a combination of higher than wild-type death rate following etoposide treatment and a lower than wild-type growth rate in the absence of treatment. In other words, the XRCC5 KO cells are not accumulating in the treated condition due to low drug-induced death, but rather dropping out of the untreated condition due to slow growth. Furthermore, the slow growth phenotype has masked the true effect of XRCC5 KO on the drug response, which is to increase the drug-induced death rate. To validate these MEDUSA-generated predictions, we cloned XRCC5 targeting sgRNAs to knockout XRCC5 and test its effect on the growth rate and death rate of U2OS cells in the presence and absence of etoposide. These data confirmed the MEDUSA-generated predictions: XRCC5 KO cells grow at slow rates in untreated conditions and die at high rates when exposed to etoposide (Figure 2G).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/67892/67892fig01.jpg" /> 
Figure 1: Overview of the MEDUSA method. (A) Schematic of a chemo-genetic profiling experiment, in which a population of cells containing single gene knockouts (SGKO) are separated into two groups, one receiving drug treatment and the other left untreated. (B) Conventional logic for the core comparison used in chemo-genetic profiling. Typically, the effect of a given gene&#39;s knockout (KO) is inferred from the relative abundance of the KO when comparing treated and untreated groups. This appears to be a comparison of 2 populations. (C) Due to different group sizes, different starting abundances for each KO clone, and variations in sequencing depth, the KO abundance is generally normalized to an internal control, such as the abundance of the cells containing non-targeting sgRNAS (WT). A log-fold change (log-2 fold-change, L2FC) analysis of this four-population comparison does not generate a quantitative insight that is necessarily related to the effect of the KO on the treatment group. Altered dynamics for any or all of the four populations contribute to the overall score, confounding the intended comparison. (D) MEDUSA aims to map the relationship between the observed L2FC and the underlying growth and death rates by simulating population dynamics. (E) A conceptual step-by-step protocol for MEDUSA. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/67892/67892fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/67892/67892fig02.jpg" /> 
Figure 2: Parameterizing a chemo-genetic profiling experiment for MEDUSA-based analysis. (A) FLICK-based evaluation of etoposide lethality in U2OS cells. (B) Hemocytometer-based cell counting to establish the size of the recoverable population following drug exposure, washing, trypsinization, and pelleting. (C) GRADE-based analysis of drug-induced growth rates and death rates for 5 &#181;M etoposide in U2OS cells. <img alt="Equation 1" src="/files/ftp_upload/67892/67892eq01.jpg" style="margin: auto;" /> = average growth rate, <img alt="Equation 2" src="/files/ftp_upload/67892/67892eq02.jpg" style="margin: auto;" /> = average death rate. (D) MEDUSA simulations of untreated and drug-treated conditions with key parameters shown. NPG = net population growth rate, GRdrug1 = drug-induced growth rate before death onset, DO = death onset time, GRdrug2 = drug-induced growth rate after death onset, DRdrug = drug-induced death rate. (E) Resulting phase diagram of the relationship between log-fold change (log-2 fold-change, L2FC) and the underlying growth and death rates. (F) MEDUSA-based analysis for every gene knockout&#39;s effect on etoposide sensitivity. (G) FLICK-based validation of XRCC5 knockout in U2OS cells.&#160;For plots with error bars, data are mean &#177; standard deviation from 3 biological replicates. GR = growth rate, DR = death rate. This figure has been modified from5. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/67892/67892fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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.

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

Systematic screening of gain- or loss-of-function genetic perturbations can be used to characterize the genetic dependencies and mechanisms of regulation ...

medusa-for-identifying-death-regulatory-genes-chemo-genetic-profiling

Nanyang Technological University

Research

JoVE Journal

Cancer Research

326 Views.  UMass Chan Medical School. Ons onderzoeksprogramma is gericht op het begrijpen hoe verschillende vormen van celdood worden gereguleerd. Over het algemeen bestuderen we celdood in de context van kankertherapieën met als doel te leren hoe medicijnen werken om celdood aan te zetten en hoe deze reacties beter en specifieker kunnen worden gemaakt. Het wordt steeds duidelijker dat er veel verschillende soorten gereguleerde celdood zijn.Het veld heeft momenteel ten minste 14 mechanist...

Video: MEDUSA voor het identificeren van doodregulerende genen in chemo-genetische profileringsgegevens

Analysis of Combinatorial miRNA Treatments to Regulate Cell Cycle and Angiogenesis

Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Similar to other cancer cells, a fundamental characteristic of LC cells is unregulated proliferation and cell division. Inhibition of proliferation by halting cell cycle progression has been shown to be a promising approach for cancer treatment, including LC. 
miRNA therapeutics have emerged as important post-transcriptional gene regulators and are increasingly being studied for use in cancer treatment. In recent work, we utilized two miRNAs, miR-143 and miR-506, to regulate cell cycle progression. A549 non-small cell lung cancer (NSCLC) cells were transfected, gene expression alterations were analyzed, and apoptotic activity due to the treatment was finally analyzed. Downregulation of cyclin-dependent kinases (CDKs) were detected (i.e., CDK1, CDK4 and CDK6), and cell cycle halted at the G1/S and G2/M phase transitions. Pathway analysis indicated potential antiangiogenic activity of the treatment, which endows the approach with multifaceted activity. Here, described are the methodologies used to identify miRNA activity regarding cell cycle inhibition, induction of apoptosis, and effects of treatment on endothelial cells by inhibition of angiogenesis. It is hoped that the methods presented here will support future research on miRNA therapeutics and corresponding activity and that the representative data will guide other researchers during experimental analyses.

miRNA therapeutics have significant potential in regulating cancer progression. Demonstrated here are analytical approaches used for identification of the activity of a combinatorial miRNA treatment in halting cell cycle and angiogenesis.

Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Similar to other cancer cells, a fundamental characteristic of LC cells is ...

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Differential gene expression analysis is an important technique for understanding disease states. The machine learning algorithm CorEx has shown utility in analyzing differential expression of groups of genes in tumor RNA-seq in a way that may be helpful for advancing precision oncology. However, CorEx produces many factors that can be challenging to analyze and connect to existing understanding. To facilitate such connections, we have built a website, CorExplorer, that allows users to interactively explore the data and answer common questions related to its analysis. We trained CorEx on RNA-seq gene expression data for four tumor types: ovarian, lung, melanoma, and colorectal. We then incorporated corresponding survival, protein-protein interactions, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments, and heatmaps into the website for association with the factor graph visualization. Here we employ example protocols to illustrate use of the database for comprehending the significance of the learned tumor factors in the context of this external data.

We introduce the CorExplorer web portal, an resource for exploration of tumor RNA sequencing factors found by the machine learning algorithm CorEx (Correlation Explanation), and show how factors can be analyzed relative to survival, database annotations, protein-protein interactions, and one another to gain insight into tumor biology and therapeutic interventions.

Differential gene expression analysis is an important technique for understanding disease states. The machine learning algorithm CorEx has shown utility ...

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Notch signaling is a highly conserved regulatory pathway involved in many cellular processes. Dysregulation of this signaling pathway often leads to interference with proper development and may even result in initiation or progression of cancers in certain cases. Because this pathway serves complex and versatile functions, it can be studied extensively through many different approaches. Of these, bioinformatics provides an undeniably cost-efficient, approachable, and user-friendly method of study. Bioinformatics is a useful way to extract smaller pieces of information from large-scale datasets. Through the implementation of various bioinformatics approaches, researchers can quickly, reliably, and efficiently interpret these large datasets, yielding insightful applications and scientific discoveries. Here, a protocol is presented for integration of bioinformatics approaches to investigate the role of Notch signaling in ovarian cancer. Furthermore, bioinformatics findings are validated through experimentation.

Bioinformatics is a useful way to process large-scale datasets. Through the implementation of bioinformatics approaches, researchers can quickly, reliably, and efficiently obtain insightful applications and scientific discoveries. This article demonstrates the utilization of bioinformatics in ovarian cancer research. It also successfully validates bioinformatics findings through experimentation.

Notch signaling is a highly conserved regulatory pathway involved in many cellular processes. Dysregulation of this signaling pathway often leads to ...

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

We present here an integrative approach for testing efficacy of targeted therapies that combines the next generation sequencing technolo-gies, therapeutic target analyses and drug response monitoring using patient derived xenografts (PDX). This strategy was validated using ovarian tumors as an example. The mate-pair next generation sequencing (MPseq) protocol was used to identify structural alterations and followed by analysis of potentially targetable alterations. Human tumors grown in immunocompromised mice were treated with drugs selected based on the genomic analyses. Results demonstrated a good correlation between the predicted and the observed responses in the PDX model. The presented approach can be used to test the efficacy of combination treatments and aid personalized treatment for patients with recurrent cancer, specifically in cases when standard therapy fails and there is a need to use drugs off label.

Here we present a protocol to test the efficacy of targeted therapies selected based on the genomic makeup of a tumor. The protocol describes identification and validation of structural DNA rearrangements, engraftment of patients&#8217; tumors into mice and testing responses to corresponding drugs.

We present here an integrative approach for testing efficacy of targeted therapies that combines the next generation sequencing technolo-gies, therapeutic ...

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Cancer stem cells play a vital role against clinical therapies, contributing to tumor relapse. There are many oncogenes involved in tumorigenesis and the initiation of cancer stemness properties. Since gene expression in the formation of colorectal cancer-derived tumorspheres is unclear, it takes time to discover the mechanisms working on one gene at a time. This study demonstrates a method to quickly discover the driver genes involved in the survival of the colorectal cancer stem-like cells in vitro. Colorectal HT29 cancer cells that express the LGR5 when cultured as spheroids and accompany an increase CD133 stemness markers were selected and used in this study. The protocol presented is used to perform RNAseq with available bioinformatics to quickly uncover the overexpressed driver genes in the formation of colorectal HT29-derived stem-like tumorspheres. The methodology can quickly screen and discover potential driver genes in other disease models.

Presented here is a protocol to discover the overexpressed driver genes maintaining the established cancer stem-like cells derived from colorectal HT29 cells. RNAseq with available bioinformatics was performed to investigate and screen gene expression networks for elucidating a potential mechanism involved in the survival of targeted tumor cells.

Cancer stem cells play a vital role against clinical therapies, contributing to tumor relapse. There are many oncogenes involved in tumorigenesis and the ...

Using the E1A Minigene Tool to Study mRNA Splicing Changes

mRNA processing involves multiple simultaneous steps to prepare mRNA for translation, such as 5&#180;capping, poly-A addition and splicing. Besides constitutive splicing, alternative mRNA splicing allows the expression of multifunctional proteins from one gene. As interactome studies are generally the first analysis for new or unknown proteins, the association of the bait protein with splicing factors is an indication that it can participate in mRNA splicing process, but to determine in what context or what genes are regulated is an empirical process. A good starting point to evaluate this function is using the classical minigene tool. Here we present the adenoviral E1A minigene usage for evaluating the alternative splicing changes after different cellular stress stimuli. We evaluated the splicing of E1A minigene in HEK293 stably overexpressing Nek4 protein after different stressing treatments. The protocol includes E1A minigene transfection, cell treatment, RNA extraction and cDNA synthesis, followed by PCR and gel analysis and quantification of the E1A spliced variants. The use of this simple and well-established method combined with specific treatments is a reliable starting point to shed light on cellular processes or what genes can be regulated by mRNA splicing.

This protocol presents a rapid and useful tool for evaluating the role of a protein with uncharacterized function in alternative splicing regulation after chemotherapeutic treatment.

mRNA processing involves multiple simultaneous steps to prepare mRNA for translation, such as 5&#180;capping, poly-A addition and splicing. Besides ...

Defining Gene Functions in Tumorigenesis by Ex vivo Ablation of Floxed Alleles in Malignant Peripheral Nerve Sheath Tumor Cells

The development of new drugs that precisely target key proteins in human cancers is fundamentally altering cancer therapeutics. However, before these drugs can be used, their target proteins must be validated as therapeutic targets in specific cancer types. This validation is often performed by knocking out the gene encoding the candidate therapeutic target in a genetically engineered mouse (GEM) model of cancer and determining what effect this has on tumor growth. Unfortunately, technical issues such as embryonic lethality in conventional knockouts and mosaicism in conditional knockouts often limit this approach. To overcome these limitations, an approach to ablating a floxed embryonic lethal gene of interest in short-term cultures of malignant peripheral nerve sheath tumors (MPNSTs) generated in a GEM model was developed.
This paper describes how to establish a mouse model with the appropriate genotype, derive short-term tumor cultures from these animals, and then ablate the floxed embryonic lethal gene using an adenoviral vector that expresses Cre recombinase and enhanced green fluorescent protein (eGFP). Purification of cells transduced with adenovirus using fluorescence-activated cell sorting (FACS) and the quantification of the effects that gene ablation exerts on cellular proliferation, viability, the transcriptome, and orthotopic allograft growth is then detailed. These methodologies provide an effective and generalizable approach to identifying and validating therapeutic targets in vitro and in vivo. These approaches also provide a renewable source of low-passage tumor-derived cells with reduced in vitro growth artifacts. This allows the biological role of the targeted gene to be studied in diverse biologic processes such as migration, invasion, metastasis, and intercellular communication mediated by the secretome.

Defining Gene Functions in Tumorigenesis by Ex vivo Ablation of Floxed Alleles in Malignant Peripheral Nerve Sheath Tumor Cells

Here, we present a protocol for performing gene knockouts that are embryonic lethal in vivo in genetically engineered mouse model-derived tumors and then assessing the effect that the knockout has on tumor growth, proliferation, survival, migration, invasion, and the transcriptome in vitro and in vivo.

The development of new drugs that precisely target key proteins in human cancers is fundamentally altering cancer therapeutics. However, before these ...

A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis

Metastasis is a complex process, requiring cells to overcome barriers that are only incompletely modeled by in vitro assays. A systematic workflow was established using robust, reproducible in vivo models and standardized methods to identify novel players in melanoma metastasis. This approach allows for data inference at specific experimental stages to precisely characterize a gene's role in metastasis. Models are established by introducing genetically modified melanoma cells via intracardiac, intradermal, or subcutaneous injections into mice, followed by monitoring with serial in vivo imaging. Once preestablished endpoints are reached, primary tumors and/or metastases-bearing organs are harvested and processed for various analyses. Tumor cells can be sorted and subjected to any of several 'omics' platforms, including single-cell RNA sequencing. Organs undergo imaging and immunohistopathological analyses to quantify the overall burden of metastases and map their specific anatomic location. This optimized pipeline, including standardized protocols for engraftment, monitoring, tissue harvesting, processing, and analysis, can be adopted for patient-derived, short-term cultures and established human and murine cell lines of various solid cancer types.

This article describes a workflow of techniques employed for testing novel candidate mediators of melanoma metastasis and their mechanism(s) of action.

Metastasis is a complex process, requiring cells to overcome barriers that are only incompletely modeled by in vitro assays. A systematic workflow was ...

Genome-Scale Screening to Identify and Compare Therapeutic Targets in Tumors

Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor

Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are derived from Schwann cells or their precursors. In patients with the tumor susceptibility syndrome neurofibromatosis type 1 (NF1), MPNSTs are the most common malignancy and the leading cause of death. These rare and aggressive soft-tissue sarcomas offer a stark future, with 5-year disease-free survival rates of 34-60%. Treatment options for individuals with MPNSTs are disappointingly limited, with disfiguring surgery being the foremost treatment option. Many once-promising therapies such as tipifarnib, an inhibitor of Ras signaling, have failed clinically. Likewise, phase II clinical trials with erlotinib, which targets the epidermal growth factor (EFGR), and sorafenib, which targets the vascular endothelial growth factor receptor (VEGF), platelet-derived growth factor receptor (PDGF), and Raf, in combination with standard chemotherapy, have also failed to produce a response in patients. 
In recent years, functional genomic screening methods combined with genetic profiling of cancer cell lines have proven useful for identifying essential cytoplasmic signaling pathways and the development of target-specific therapies. In the case of rare tumor types, a variation of this approach known as cross-species comparative oncogenomics is increasingly being used to identify novel therapeutic targets. In cross-species comparative oncogenomics, genetic profiling and functional genomics are performed in genetically engineered mouse (GEM) models and the results are then validated in the rare human specimens and cell lines that are available. 
This paper describes how to identify candidate driver gene mutations in human and mouse MPNST cells using whole exome sequencing (WES). We then describe how to perform genome-scale shRNA screens to identify and compare critical signaling pathways in mouse and human MPNST cells and identify druggable targets in these pathways. These methodologies provide an effective approach to identifying new therapeutic targets in a variety of human cancer types.

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens 

We have developed a cross-species comparative oncogenomics approach utilizing genomic analyses and functional genomic screens to identify and compare therapeutic targets in tumors arising in genetically engineered mouse models and the corresponding human tumor type.

Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are derived from Schwann cells or their precursors. In patients with the tumor susceptibility syndrome ...

Pipeline for Analyzing Microarray Data in Esophageal Cancer Research

Development of Compendium for Esophageal Squamous Cell Carcinoma

Esophageal cancer (EC) ranks as the 8th most aggressive malignancy, and its treatment remains challenging due to the lack of biomarkers facilitating early detection. EC manifests in two major histological forms - adenocarcinoma (EAD) and squamous cell carcinoma (ESCC) - both exhibiting variations in incidence across geographically distinct populations. High-throughput technologies are transforming the understanding of diseases, including cancer. A significant challenge for the scientific community is dealing with scattered data in the literature. To address this, a simple pipeline is proposed for the analysis of publicly available microarray datasets and the collection of differentially regulated molecules between cancer and normal conditions. The pipeline can serve as a standard approach for differential gene expression analysis, identifying genes differentially expressed between cancer and normal tissues or among different cancer subtypes. The pipeline involves several steps, including Data preprocessing (involving quality control and normalization of raw gene expression data to remove technical variations between samples), Differential expression analysis (identifying genes differentially expressed between two or more groups of samples using statistical tests such as t-tests, ANOVA, or linear models), Functional analysis (using bioinformatics tools to identify enriched biological pathways and functions in differentially expressed genes), and Validation (involving validation using independent datasets or experimental methods such as qPCR or immunohistochemistry). Using this pipeline, a collection of differentially expressed molecules (DEMs) can be generated for any type of cancer, including esophageal cancer. This compendium can be utilized to identify potential biomarkers and drug targets for cancer and enhance understanding of the molecular mechanisms underlying the disease. Additionally, population-specific screening of esophageal cancer using this pipeline will help identify specific drug targets for distinct populations, leading to personalized treatments for the disease.

This protocol describes a useful tool for identifying significant molecular changes in cancer and leads to the development of new diagnostic and therapeutic approaches for esophageal squamous cell carcinoma.

Esophageal cancer (EC) ranks as the 8th most aggressive malignancy, and its treatment remains challenging due to the lack of biomarkers facilitating early ...

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

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