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This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) 1003- 216S461 and 1001-221S419 Grants. Schematic illustration figures were created with BioRender.com and licensed for publication (Figure 1: LB29MV4E2Q, Figure 2: JZ29CC68WD, Figure 3: BV29CC72LB, Figure 4: BN29CC6XFH). The authors acknowledge the use of ChatGPT (OpenAI) solely for grammatical editing and English language enhancement. The tool did not contribute to the generation of scientific content. We gratefully acknowledge the use of the services and facilities of the Ko&#231; University Research Center for Translational Medicine (KUTTAM), funded by the Presidency of Turkey, Head of Strategy and Budget.

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The authors declare no conflict of interest.

CRISPR-Cas9-based pooled screening approaches are well established in different cellular contexts. However, screening in chemoresistant cells presents distinct challenges and requires additional optimization. Since chemoresistant cells serve as valuable models to study the vulnerabilities of chemoresistant tumors, it is important to address these limitations in screening strategies to reliably identify genetic or epigenetic regulators of chemoresistance.
Generation of chemoresistant cell lines itself may be cumbersome and is highly dependent on the chemotherapeutic agent used. While cells may readily adapt to certain drugs, they can be highly susceptible to others25. Therefore, it is important to determine cell line-specific IC50 values prior to the resistant cell line generation protocol. In cases where cells fail to tolerate IC50-level drug exposure over several passages, it is possible to start with a lower drug concentration (e.g., IC10-20) (see Protocol section 1)17. Since the acquisition of resistance is a stochastic event, several wells/plates of cells might be treated at the same time to increase the likelihood of obtaining resistant cells. It is crucial to comprehensively characterize the obtained drug-resistant cell population in order to describe transcriptional and phenotypic changes during the dose-escalation procedure in comparison to their na&#239;ve state. Global profiling approaches such as RNA-seq can be employed to identify differentially expressed genes relative to the parental line. Functionally, acquired drug resistance should be validated with cell viability and proliferation assays to demonstrate that resistant cells maintain survival under drug treatment conditions that are cytotoxic to parental cells. Also, potential changes in their proliferative capacity and growth rate should be measured in comparison to the parental cell line to calculate their respective PDLs. Since acquired Taxol resistance is known to be mediated, at least in part, by ABC-mediated drug efflux, the expression of key ABC transporters (e.g., ABCB1) should be evaluated at the mRNA and protein levels using qPCR and Western blotting, and can be complemented by transcriptomic data. Based on the ABC transporter expression profile, the presence of MDR phenotype should be considered, and resistance to other chemotherapeutic agents should be assessed. In our previous study, we characterized the Taxol-resistant cell lines and identified ABCB1 as one of the most upregulated genes, and we reasoned that ABCB1-mediated multidrug resistance can cause failure in puromycin selection17. Therefore, after resistant cell generation, expression of ABC transporters and MDR phenotype must be assessed before any transfection procedure.
The dose-escalation method offers advantages over high-dose drug exposure protocols, as cells can be cryopreserved at each step before increasing the drug dose, providing flexibility and safeguarding against culture loss. Moreover, gradual dose escalation allows acquisition of resistance phenotypes over time, enabling adaptive evolution; in contrast to acute high-dose treatment, which results in selection of pre-existing resistant subpopulations. Its stepwise progression enables researchers to deliberately choose the desired resistance level at which to stop selection and evaluate the properties of the resulting phenotype. Also, the intermediate steps of the dose escalation could be leveraged to study mechanisms of acquired resistance in a time-course manner. According to the underlying biological question, different resistant levels could be obtained to study either early adaptive mechanisms with lower resistance levels or stable and fully established resistance phenotypes in more resistant cells. Moreover, clinically relevant resistance thresholds vary substantially depending on the drug, cell line, and intrinsic proliferation rate; therefore, the timeline of dose escalation may differ accordingly (in our case, the generation of Taxol-resistant cells required approximately 6 months). Consistent with this rationale, we established two different levels of resistance with SUM159PT cells in our previous study and compared their distinct and shared features17. Therefore, we recommend that researchers define the target IC50 range of the drug of interest according to clinical and biological relevance.
It is well established that withdrawal of drug pressure may lead to loss of chemoresistance, as cells may rewire their epigenome and transcriptome25. For this reason, it is suggested to assess the stability of the resistance phenotype over several months. If cells tend to revert back to a sensitive state upon drug withdrawal, resistant cells may be cultured in the presence of the maintenance dose, typically corresponding to the final drug concentration used during resistance generation, to preserve their resistance phenotype. While in vitro chemoresistant cell line models are practical, reproducible, and widely used for studying acquired chemoresistance, they may not fully recapitulate the phenotypic heterogeneity observed in patient tumors and lack the tumor microenvironment, immune interactions, and spatial heterogeneity3,4,25. Furthermore, prolonged passaging during the resistance generation can cause genetic and epigenetic alterations diverging from those observed in patient samples, potentially limiting the translational relevance of identified hits. Therefore, candidate genes and mechanisms identified through this protocol should be further validated in more clinically relevant experimental systems, such as co-culture models, patient-derived organoids, or in vivo studies. EPIKOL provides advantages with its focused nature if complementary screens are to be performed on these models, where the maximum number of cells that can be obtained is inherently limited.
Chemoresistant cells often grow more slowly than their parental counterparts, which can pose a practical challenge for genome-wide screens that require large cell populations to maintain library coverage. Focused libraries such as EPIKOL offer an advantage to perform a screen with less amount of starting material while providing increased sgRNA depth per gene, thereby improving sensitivity and reproducibility within the defined target space. However, by design, EPIKOL only captures epigenetic resistance mechanisms and does not cover non-epigenetic contributors to chemoresistance. The choice of library should therefore be guided by prior experiments and the specific biological question. Users of these libraries should be aware of the limitations of the focused libraries and should interpret the results within the scope of the library.
A major barrier in CRISPR screening of chemoresistant models is the emergence of MDR phenotype driven by ABC transporter upregulation9,10. Different ABC transporters mediate the efflux of different substrates. Once a drug-resistant cell line is generated, it is important to assess whether resistance extends to other chemotherapeutic agents. Puromycin resistance is mainly caused by ABCB1 overexpression, and this affects the selection process of the pooled CRISPR screening approaches, as most of these libraries carry puromycin selection cassettes for enrichment of transduced cells. Here, we utilized verapamil, a first-generation ABCB1 inhibitor, to facilitate intracellular accumulation of puromycin and selectively eliminate the untransduced resistant cells that would otherwise escape the selection due to ABCB1-mediated drug efflux. Since verapamil binds ABCB1 reversibly, ABCB1 function is restored within hours upon removal of verapamil. Therefore, verapamil does not confer an additional vulnerability to resistant cells during the subsequent drug treatment phase of the screen. However, several limitations of verapamil should be acknowledged. Since verapamil is an inhibitor of calcium channels and other transporters, it can influence cellular physiology beyond ABCB1 inhibition10. Additionally, effective verapamil concentration varies across cell lines and MDR models depending on the level of ABCB1 expression and the activity of other transporters. Therefore, verapamil concentration should be optimized for each cell line, and a new generation of ABCB1 inhibitors more specific to ABCB1 may be considered if off-target effects are a concern. As another critical step, viral titering should be performed on the resistant cell itself rather than the parental counterparts, as the infectivity and selectability often differ for resistant cells.
Another important consideration is to determine the appropriate duration of the screen, as chemoresistant cells often grow more slowly than their matched parental counterparts26. To account for this difference, we utilized population doubling level calculations (Protocol section 6) to assess when the control and treatment groups reached the desired PDL threshold. Screens may therefore be terminated on different days, allowing comparable population doublings and enabling safer interpretation of sgRNA effects in the treatment group.
Library preparation PCRs are considerably important for the accuracy of the downstream hit identification. Bias introduced by low gDNA input and increased PCR cycles may lead to amplification of certain sgRNA sequences, causing false-positive enrichments or depletions. To mitigate this, we recommend using the maximum amount of gDNA possible, adhering to the PCR cycle numbers that are offered by this protocol, and running parallel reactions to pool before sequencing. When possible, library coverage should not fall below the recommended thresholds (&#62;300&#215; coverage) to ensure reliable detection of depleted sgRNAs rather than stochastic dropout of low-abundance sgRNAs due to low coverage.
Following sequencing, we used the MAGeCK analysis framework and performed several downstream quality control analyses and data interpretation steps24. First, principal component analysis (PCA) was used to assess the distribution and reproducibility of biological replicates (data not shown, MAGeCK output). Replicate clustering in PCA is expected within each arm; however, higher variability in the drug-treated arm is a common feature of chemoresistance screens, as the drug can induce stochastic changes. If the positive and negative controls behave as expected in each arm, this variability does not prevent identification of true-positive hits. Next, reads per million (RPM) normalization of sgRNA counts at the initial and final timepoints was applied to evaluate whether sgRNAs targeting essential genes were depleted as expected14. Subsequently, median normalization was used to aggregate sgRNA-level effects, enabling identification of gene-level depletions24. Gene-level depletion scores were visualized using waterfall plots, which rank genes from the most depleted to the enriched ones. Copy number alterations and polyploidy arising from prolonged drug exposure can confound screen results through inefficient or excessive Cas9 cutting27. While EPIKOL targets chromatin regulators that are generally not subject to Taxol-driven amplifications, we recommend assessing ploidy status and copy number changes in the resistant cells before screening. If copy number changes are suspected, computational correction tools such as CRISPRcleanR or CERES can be applied during MAGeCK analysis.
In screens involving two experimental arms, control and treatment groups, different comparisons are required to properly interpret screen performance and biological dependencies17. Initial-to-final comparisons are suited for assessing screen performance, including expected behavior of essential genes and non-targeting controls. Comparison of the endpoint of the control group (DMSO) with the initial time point allows identification of genes required for the growth and fitness of resistant cells in the absence of drug pressure. In contrast, comparison of two endpoint samples, drug-treated (Taxol) and control (DMSO), reveals the specific genes that were required for resistance to the drug rather than general fitness. It should be noted that direct comparison of the two endpoint samples leads to a skewed distribution of essential gene controls, as they are required for survival in both groups. An alternative visualization method might be the Volcano plot, which displays log2 fold changes together with significance values, enabling identification of significantly depleted genes. Once the candidate genes are identified, normalized sgRNA counts and visualization plots are useful to decide on the best-performing sgRNAs for downstream validation assays. For the EPIKOL screen, we recommend prioritizing candidate genes supported by the concordant behavior of at least 6 out of 10 sgRNAs, as this provides greater confidence in gene-level effects.
Here, we provide an optimized protocol for performing EPIKOL screening on Taxol-resistant cell lines that have upregulated ABCB1 and exhibited MDR phenotype. This protocol is broadly applicable to any chemoresistant model that displays cross-resistance to puromycin or other selection antibiotics, thereby enabling CRISPR screening in these cells. Practical guidance for the most common technical challenges encountered across all protocol steps is provided in Supplementary Table 1.

Triple negative breast cancer (TNBC) is a subtype of breast cancer that accounts for 12&#8211;17% of all breast cancers1. It is characterized by the impaired expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2), which severely limits the availability of targeted therapy options and leaves conventional chemotherapy as the primary treatment modality2. Taxanes and anthracyclines are commonly used in TNBC treatment; however, patients usually develop resistance2,3. Acquisition of chemoresistance is a complex and multidimensional process that includes both genetic, metabolic, transcriptional, and epigenetic adaptations2,4. Epigenetic alterations have emerged as central regulators of cellular plasticity, facilitating dynamic transcriptional reprogramming that enables cancer cells to tolerate chemotherapy-induced toxicity5.
Taxol (paclitaxel) is a member of the taxane class, which causes cell death by impairing the microtubule dynamics and functions as an antimitotic agent6. Even though TNBC patients initially respond to the chemotherapy regimens including Taxol, resistance usually develops over time2. Acquired Taxol resistance is commonly associated with overexpression of ATP-binding cassette (ABC) transporters, specifically ABCB1 (MDR1, P-glycoprotein), which mediate the drug efflux of Taxol, resulting in decreased intracellular drug toxicity7. Since a wide range of chemotherapeutic agents are substrates of these ABC transporters, their upregulation limits the use of other chemotherapeutic drugs, causing a multidrug resistance phenotype (MDR)8. Inhibition of ABC transporters has proven largely unsuccessful as a strategy to overcome multidrug resistance in clinical settings, primarily due to their ubiquitous expression and essential roles in transporting a wide range of endogenous and exogenous substrates9. Although verapamil, a first-generation ABCB1 inhibitor, demonstrated limited or no clinical benefit and was associated with significant toxicity in clinical trials10, it remains a valuable experimental tool for enhancing selection efficiency in in vitro models of MDR.
MDR phenotype is a dynamic and adaptive process that mostly emerges during stress conditions2. ABC transporter expression is predominantly controlled by transcriptional and epigenetic regulatory mechanisms, rather than by mutational events5,11. The limitation of directly targeting ABC transporters highlights the need to identify the upstream regulators of these proteins as a vulnerability specific to the resistant cells. Therefore, systematic interrogation of epigenetic factors has become an important strategy to identify novel regulators of chemotherapy resistance and to develop combination therapies targeting these vulnerabilities.
CRISPR-Cas9-based functional knockout screens enable unbiased, systematic genome-wide perturbations to study effects of gene loss in cancer at the genome scale12. However, genome-wide CRISPR libraries offer limited single guide RNA (sgRNA) representation per gene, which reduces sensitivity when compared to focused libraries13. Importantly, a robust screen not only depends on the quality of the perturbations but also on an appropriate model and an optimized experimental protocol. In this context, studying a dynamic and complex process such as drug resistance might benefit from a focused library with a customized protocol13. In a previous work, we generated an epigenome-focused CRISPR-Cas9 knockout library to target chromatin regulators14. Compared to other publicly available epigenome-focused CRISPR libraries, epigenetic knockout library (EPIKOL) contains 779 genes, consisting of chromatin readers, writers, erasers, and associated proteins, each targeted with 10 sgRNAs, thereby enabling increasing depth per gene and improving robustness and reproducibility of chromatin-focused functional screens14. EPIKOL is available in both the LentiGuide system, in which Cas9 is expressed from a different vector, and the LentiCRISPR system, which includes Cas9, providing flexibility depending on the cell line. Reaching sufficient library coverage, defined as the average number of cells per sgRNA (e.g., 500&#215;&#8211;1000&#215;) is easier for EPIKOL when compared to genome-wide libraries, providing an advantage while studying the slow-growing chemoresistant cell lines.
While CRISPR screening offers a powerful approach to examine chemoresistance mechanisms, standard pooled screening workflows are not optimized for chemoresistant cells with active drug efflux. Puromycin, a commonly used selection antibiotic in lentiviral CRISPR systems, can be pumped out of the resistant cells by ABC transporters, mainly ABCB1, preventing successful selection of transduced cells15,16. This limitation can lead to heterogeneous cell populations, increasing background noise, and ultimately compromising the data quality. To address this challenge, we optimized the puromycin selection step by transiently inhibiting ABC-mediated drug efflux with verapamil, thereby enabling efficient enrichment of sgRNA-infected cells. Using this strategy, we identified Bromodomain and PHD Finger Containing 1 (BRPF1) as a regulator of ABCB1 expression and Taxol resistance in TNBC17. BRPF1 inhibition, either genetically or pharmacologically, restored the Taxol sensitivity in resistant cells, demonstrating the utility of this optimized protocol in identifying actionable targets in chemoresistance17.
Here, we outline the key steps in negative selection CRISPR screening on chemoresistant models, specifically focusing on the technical challenges that are often overlooked in most chemoresistance or CRISPR screening protocols. The protocol first demonstrates a stepwise dose escalation method for the generation of chemoresistant cells and then optimizes viral delivery and selection conditions to overcome ABC transporter-mediated drug efflux. Together, these optimizations ensure efficient enrichment of sgRNA-positive cells during the CRISPR screens, while effectively eliminating the non-transduced cells, thereby providing a robust and efficient strategy for transduction and selection in chemoresistant TNBC cells. Finally, the protocol highlights the fundamentals of identifying CRISPR screening hits through next-generation sequencing and bioinformatic analysis.&#160; Together, this protocol enables effective implementation of the EPIKOL screening platform to identify potential therapeutic targets to overcome resistance17,18,19.

<table><tbody><tr><td>100 mm plate</td><td>Corning</td><td>353003</td><td>Cell culture</td></tr><tr><td>45 &amp;micro;m filter&amp;nbsp;</td><td>Sigma-Aldrich</td><td>SLHVM33RS</td><td>Cell culture</td></tr><tr><td>6 well plate</td><td>Corning</td><td>3506</td><td>Cell culture</td></tr><tr><td>96-well black clear bottom plates</td><td>Sigma-Aldrich</td><td>CLS356259</td><td>For CellTiter-Glo Luminescent Cell Viability Assay</td></tr><tr><td>Blasticidin</td><td>Thermo Fisher</td><td>A1113903</td><td>For selection</td></tr><tr><td>CellTiter-Glo luminescent cell viability assay</td><td>CellTiter-Glo, Promega</td><td>G7570</td><td>For CellTiter-Glo Luminescent Cell Viability Assay</td></tr><tr><td>DMEM</td><td>Gibco</td><td></td><td>Cell culture</td></tr><tr><td>DMSO</td><td>PanReac AppliChem</td><td>A3672,0050</td><td>Cell culture</td></tr><tr><td>FBS</td><td>Gibco</td><td>16140071</td><td>Cell culture</td></tr><tr><td>Ham&amp;rsquo;s F12 nutrient mix</td><td>Gibco</td><td>11765054</td><td>Cell culture</td></tr><tr><td>HEK293T cells</td><td></td><td></td><td>Kind gifts from Robert Weinberg (MIT, Boston, USA).</td></tr><tr><td>HEPES</td><td>Thermo Fisher</td><td>15630080</td><td>Cell culture</td></tr><tr><td>Hydrocortisone</td><td>Sigma-Aldrich</td><td>H4001-1G</td><td>Cell culture</td></tr><tr><td>Insulin</td><td>Sigma-Aldrich</td><td>I9278-5ML</td><td>Cell culture</td></tr><tr><td>LentiCas9-blast</td><td>Addgene&amp;nbsp;</td><td>#52962</td><td>For Cas9-stable cell line generation</td></tr><tr><td>LentiGuide-Puro&amp;nbsp;</td><td>Addgene&amp;nbsp;</td><td>#52963</td><td>Backbone for gRNA cloning&amp;nbsp;</td></tr><tr><td>MN NucleoSpin tissue kit</td><td>Macherey-Nagel</td><td>740952.5</td><td>For genomic DNA extraction</td></tr><tr><td>PBS</td><td>Gibco&amp;nbsp;</td><td>10010023</td><td>Cell culture</td></tr><tr><td>Penicillin-Streptomycin</td><td>Gibco</td><td>15140122</td><td>Cell culture</td></tr><tr><td>pLentiCRISPR v2&amp;nbsp;</td><td>Addgene&amp;nbsp;</td><td>#52961</td><td>Backbone for gRNA cloning&amp;nbsp;</td></tr><tr><td>Polyethylene glycol (PEG)-8000</td><td>Sigma-Aldrich</td><td>P2139-500G</td><td>For lentivirus concentration</td></tr><tr><td>Polyethylenimine, linear (PEI)</td><td>Sigma-Aldrich</td><td>765090-1G</td><td>For lentivirus production</td></tr><tr><td>Protamine sulfate (PS)</td><td>Sigma-Aldrich</td><td>P4380-25G</td><td>For lentivirus infection</td></tr><tr><td>psPAX2</td><td>Addgene&amp;nbsp;</td><td># 12260</td><td>For lentivirus production</td></tr><tr><td>Puromycin</td><td>Thermo Fisher</td><td>A1113803</td><td>For selection</td></tr><tr><td>SUM159PT adherent cells</td><td></td><td></td><td>Kind gifts from Robert Weinberg (MIT, Boston, USA).</td></tr><tr><td>Taxol</td><td>Paclitaxel, Sigma</td><td>PHR1803-200MG</td><td>For res cell generation and maintenance</td></tr><tr><td>Trypsin-EDTA (0.05%)</td><td>Gibco&amp;nbsp;</td><td>25300054</td><td>Cell culture</td></tr><tr><td>Verapamil</td><td>Selleck Chemicals</td><td>S4202</td><td>For selection</td></tr><tr><td>VSV-G</td><td>Addgene&amp;nbsp;</td><td># 8454</td><td>For lentivirus production</td></tr></tbody></table>

epigenome-wide crispr-cas9-based knockout screens on chemoresistant cells

Epigenome-Wide CRISPR-Cas9-Based Knockout Screens on Chemoresistant Cells

Chemotherapy resistance remains a major challenge in cancer treatment, driven by cancer cells&#39; ability to acquire adaptive properties, rewire signaling pathways, and alter chromatin structure to evade drug-induced cytotoxicity. Because these processes rely heavily on epigenetic mechanisms that regulate chromatin organization and transcriptional plasticity, epigenetic regulators have emerged as key contributors to chemotherapy resistance. To investigate resistance to paclitaxel, one of the most widely used chemotherapeutic agents in triple-negative breast cancer (TNBC), we employed an epigenome-focused knockout library (EPIKOL), a CRISPR-Cas9&#8211;based library, designed to systematically disrupt genes involved in chromatin regulation. Chemoresistant cell lines were generated through a stepwise dose-escalation protocol that recapitulates clinically relevant drug adaptation. However, these resistant cells exhibit a multidrug-resistant (MDR) phenotype, posing significant challenges for efficient viral transduction and the selection of stable cell populations. In this study, we describe key methodological steps for achieving high-efficiency lentiviral transduction and selection, enabling the successful application of EPIKOL CRISPR screens in chemoresistant TNBC models. Following the described protocol, an epigenome-wide CRISPR screen was conducted on chemoresistant TNBC cells, and novel epigenetic regulators of chemoresistance were identified. This protocol provides a robust framework for identifying epigenetic regulators that contribute to acquired paclitaxel resistance using a CRISPR-based loss-of-function approach.

To study epigenetic regulators of chemoresistance in TNBC, the overall protocol using the SUM159PT cell line is shown in Figure 1. First, IC10 and IC50 values of Taxol in SUM159PT cells were determined by treating the cells with increasing concentrations of Taxol (Figure 2A). A stepwise dose escalation method was used to generate Taxol-resistant SUM159PT cells. After several months, a shift in IC50 values illustrates the successful establishment of a resistant phenotype using this protocol when compared to control cells that were passaged in parallel without Taxol exposure (Figure 2B).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/71175/71175fig1.jpg" /> 
Figure 1: Schematic of the EPIKOL screen in Taxol-resistant SUM159PT cells. Overall workflow of EPIKOL screening in the generated Taxol-resistant SUM159PT cells. The figure was created with BioRender.com <a href="https://www.jove.com/files/ftp_upload/71175/71175fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Once the resistant cells were established, several experiments were performed to characterize their phenotypic and molecular changes. Proliferation capacities of resistant cells under Taxol treatment were assessed with a colony formation assay in comparison to parental cells. As seen in Figure 2C,D, resistant cells maintain their proliferative capacity under Taxol treatment while parental cells cannot survive. This assay is one of the important steps in resistant cell characterization and demonstrates how proliferative capacity under drug treatment can be assessed between parental and resistant cells.
As a part of the resistant cell characterization, RNA sequencing was performed on parental and resistant cells to analyze the transcriptomic changes associated with chemoresistance (Figure 2E). Among the most upregulated genes in resistant cells was ABCB1, and it was followed by a couple of other ABC transporters, including ABCB4 and ABCA5. The upregulation of ABCB1 expression was validated with quantitative polymerase chain reaction (qPCR), and this illustrates how candidate gene expression changes can be validated using qPCR (Figure 2F).
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/71175/71175fig2.jpg" /> 
Figure 2: Generation of resistant cells and their characterization. (A) Schematic showing the generation of Taxol-resistant SUM159PT cells with the dose escalation method. (B) Dose response curve showing the difference between IC50 values of parental and generated resistant cells. Parental IC50 = 6 nM and Taxol-resistant IC50 = 355.4 nM. (C) Colony formation assay in the presence of Taxol. (D) Quantification of colony areas in (C.) Two-way analysis of variance (ANOVA) with Tukey&#39;s post hoc test was applied using Prism 8 (GraphPad Software). Significance levels were set as *P &#60; 0.05, **P &#60; 0.01, ***P &#60; 0.001. (E) Volcano plots showing differentially expressed genes in Taxol-Res cells compared to parental cells, with an LFC&#62;2 and p&#60;0.001 cutoff. (F) qPCR validation of ABCB1 mRNA expression in parental and Taxol-Res cells. P values determined by two-tailed Student&#39;s t-test in comparison with control group; **P &#60; 0.01. Panel A was created with BioRender.com. Panel E figure has been modified with permission from Yedier-Bayram et al.17. <a href="https://www.jove.com/files/ftp_upload/71175/71175fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
After showing overexpression of ABCB1 and other ABC transporters in Taxol-resistant cells and considering the broad substrate spectrum of ABCB1, the presence of MDR phenotype was assessed. To this end, Taxol-resistant cells were treated with multiple drugs that are known substrates of ABCB1 transporter and have different mechanisms of action, and their drug sensitivity was evaluated (Figure 3A,B). In line with the observed ABC transporter expression profile, Taxol-resistant cells also showed resistance to Doxorubicin and Vincristine, confirming the presence of MDR profile and illustrating how multidrug resistance phenotype can be functionally assessed.
MDR phenotype is a challenge for the CRISPR-based screening approach, EPIKOL, given that the library requires puromycin selection to enrich transduced cells. Since puromycin can be pumped out of the cells by ABCB1-mediated drug efflux, puromycin selection may not be efficient in resistant cells (Figure 3C). Hence, non-transduced cells can survive selection, confounding screen outcomes.
To address this limitation, the selection process of chemoresistant cells with an MDR profile was optimized prior to the EPIKOL screen. Verapamil, a first-generation ABCB1 inhibitor, was used to block ABC transporter-mediated drug efflux, thereby allowing intracellular accumulation of chemotherapeutic agents or puromycin (Figure 3D). While verapamil alone did not affect cell viability, it restored drug sensitivity when combined with chemotherapeutic agents (Figure 3E,F). Similarly, puromycin kill curves combined with non-toxic doses of verapamil demonstrated that ABCB1 inhibition restores puromycin sensitivity (Figure 3G,H). This ensures that cells surviving selection are those that are successfully transduced rather than cells escaping selection due to ABCB1-driven drug efflux. Establishing an effective selection workflow in chemoresistant cells is crucial for reliable EPIKOL screen outcomes.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/71175/71175fig3.jpg" /> 
Figure 3: Multidrug resistance phenotype and effect of verapamil on resistant cells. Dose response curves showing the difference between IC50 values of parental and generated resistant cells with (A) Doxorubicin, (B) Vincristine, (C) Puromycin. (D) Schematic showing the selection of Taxol-resistant SUM159PT cells with and without verapamil. The figure was created with BioRender.com. (E) Cell viability assay with Taxol-Res cells to determine optimal verapamil concentration with Taxol. (F) Verapamil resensitizes Taxol-resistant cells to Taxol. (G) Verapamil does not alter Parental cells&#39; response to puromycin. (H) Verapamil restores puromycin sensitivity in Taxol-Res cells. Panels A, B, and F have been reused with permission from Yedier-Bayram et al.17. <a href="https://www.jove.com/files/ftp_upload/71175/71175fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
To find novel regulators of chemoresistance in TNBC, an EPIKOL screen was performed with Taxol-resistant SUM159PT cells as illustrated in Figure 4A. Since resistant cells grow slower than parental cells17, population doubling levels were monitored throughout the screen in both Taxol and DMSO-treated groups to determine the time required to reach 16 population doublings (Figure 4B). Accordingly, EPIKOL screening for the DMSO-treated group was completed at approximately Day 35, while the Taxol-treated group was cultured for an additional 15 days to reach the same population doubling level.
Following sequencing, the MAGeCK analysis pipeline was used to combine the effect of sgRNAs targeting the same gene to reveal the gene-level changes upon knockout24. EPIKOL library includes sgRNAs targeting essential genes as positive controls, the first quality control step was to assess whether these sgRNAs were depleted as expected. Waterfall plots illustrate the Log2 fold changes of essential genes across different comparisons (Figure 4C&#8211;E). When final timepoint samples from DMSO- or Taxol-treated conditions were compared to the initial timepoint, almost all essential genes were depleted upon knockout, indicating effective screen performance (Figure 4C,D). However, this depletion is not clearly observed when the two endpoint samples (DMSO-treated and Taxol-treated) were compared to each other, as expected, since essential genes are required for cell survival under both conditions (Figure 4E).
An alternative visualization of the screen results is the volcano plot, which displays the Log2 fold changes together with statistical significance (the p-value or FDR values). In the EPIKOL screen performed in Taxol-resistant cells, multiple positive control genes were identified as significantly depleted, further supporting the reliability of the screen since depletion of sgRNAs targeting positive control genes illustrates a typical quality control of a successful screen (Figure 4F). In addition to essential gene controls, the EPIKOL library includes context-specific positive control sgRNAs targeting several ABC transporter genes. As expected, normalized counts of ABCB1 sgRNAs in different groups highlighted the significant depletion of ABCB1-targeting sgRNAs specifically in the Taxol-treated group (Figure 4G). Overall, this demonstrates how sgRNA-level changes can be interpreted to identify candidate genes associated with drug response.
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/71175/71175fig4.jpg" /> 
Figure 4: EPIKOL screen results in Taxol-resistant cells and example graphs. (A) Schematic of EPIKOL screen protocol. (B) Population Doubling Level (PDL) of Taxol-Res cells. (C&#8211;E) Waterfall plots showing results of EPIKOL screen on Taxol-Res cells, highlighting essential genes, DMSO-treated group compared to the Initial timepoint in panel C, Taxol-treated group compared to the Initial timepoint in panel D, Taxol-treated group compared to the DMSO-treated group in panel E. (F) Volcano plot showing Log2FoldChanges of genes in Taxol-treated samples compared to the Initial timepoint. Genes that have p&#60;0.05 were colored and labeled. This figure has been modified from a previous publication17. (G) Read per million (rpm) counts of 10 different ABCB1 sgRNA graphs from EPIKOL. Panel A was created with BioRender.com. Panel E has been reused with permission from Yedier-Bayram et al.17. <a href="https://www.jove.com/files/ftp_upload/71175/71175fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary Table 1: Troubleshooting table.<a href="https://www.jove.com/files/ftp_upload/71175/Supplementary Table 1.xlsx" target="_blank">Please click here to download this file.</a>
Supplementary Table 2: Viral Titer and MOI calculation.<a href="https://www.jove.com/files/ftp_upload/71175/Supplementary Table 2.xlsx" target="_blank">Please click here to download this file.</a>
Supplementary Table 3: sgRNA list of EPIKOL library.<a href="https://www.jove.com/files/ftp_upload/71175/Supplementary Table 3.xlsx" target="_blank">Please click here to download this file.</a>
Supplementary Table 4: Primers used in EPIKOL sequencing library preparation. <a href="https://www.jove.com/files/ftp_upload/71175/Supplementary Table 4.xlsx" target="_blank">Please click here to download this file.</a>

Here, we present a protocol for generating chemoresistant cells using a dose-escalation approach, followed by a CRISPR/Cas9-based screen using a focused sgRNA library targeting epigenetic modifiers to identify regulators of acquired chemoresistance. The protocol also provides multiple optimization points tailored to chemoresistant cell models, offering a robust framework for researchers investigating resistance mechanisms.

Chemotherapy resistance remains a major challenge in cancer treatment, driven by cancer cells&#39; ability to acquire adaptive properties, rewire ...

We investigate epigenetic mechanisms driving chemo resistance in triple negative breast cancer using CRISPR-based screening approaches. To begin, adjust some 159 PT adherent cells to 2 times 10 to the power of 3 cells per well in 96 well black-clear bottom plates with Ham's F-12 Nutrient Mix, supplemented with the presented components. Incubate the cells for 16 hours in a cell culture incubator at 37 degrees Celsius and 5%carbon dioxide.Next, prepare Taxol stock dissolved in dimethyl sulfoxide or DMSO. To treat the cells, prepare serial dilutions of Taxol in culture media, ranging from 0.3 nanomolar to 10 micromolar and use DMSO alone as a vehicle control. Treat the cells with 200 microliters of increasing doses of Taxol in triplicate.Leave the plate in a cell culture incubator for 72 hours at 37 degrees Celsius with 5%carbon dioxide. After 72 hours of drug treatment, assess cell viability using a luminescence-based ATP quantification assay. Measure the luminescence with a microplate reader and normalize the results to the DMSO control values.Next, calculate the 10%and 50%inhibitory concentration values using nonlinear regression with a four parameter logistic dose response model in suitable statistical software. To start generating chemoresistant cells, seed 2 times 10 to the power of 5 some 159 PT cells per well in six well plates. And incubate overnight at 37 degrees Celsius with 5%carbon dioxide.The next day, treat the cells with 10%or 50%inhibitory concentrations of Taxol in two milliliters of culture media for 72 hours. If the cells tolerate the current drug concentration, split them into two wells. Keep one as a backup control with fresh medium and double the Taxol concentration in the other well.While generating resistant cells, passage the parental cells in medium containing DMSO as a control and adjust the DMSO volume to match the amount present in the corresponding Taxol treatment. Measure cell viability every three to four weeks until a significant difference in the 50%inhibitory concentration values is observed between the parental and resistant cell lines. Maintain the stable resistant cells at the final Taxol concentration used during dose escalation.For characterization of resistant cells, perform RNA sequencing, Western blotting, qPCR, and cell viability assays in the presence of the drug. Seed some 159 PT Taxol-resistant and parental cells in triplicate into 96 well black-clear bottom plates at 2 times 10 to the power of 3 cells per well. Incubate the cells overnight at 37 degrees Celsius with 5%carbon dioxide.Treat the cells with twofold serial dilutions of verapamil, ranging from 80 micromolar to 1.25 micromolar, while maintaining a constant Taxol concentration across all treatment conditions. After 72 hours of treatment, assess cell viability using a luminescence-based ATP quantification assay. Determine the verapamil concentration that maintains cell viability while restoring sensitivity in resistant cells.To find the puromycin concentration to be used in combination with verapamil, seed some 159 PT Taxol-resistant cells into a 96 well plate at 2 times 10 to the power of 3 cells per well. Incubate the plate for 16 hours at 37 degrees Celsius and 5%carbon dioxide. Prepare twofold serial dilutions of puromycin, ranging from 40 to 0.625 micrograms per milliliter, in medium containing the previously determined verapamil concentration.Treat the cells with the puromycin dilutions accordingly for 72 hours. After the viability assay, identify the lowest puromycin concentration that reduces the viability to 5%or less in the presence of verapamil. Then use some 159 PT Taxol-resistant cells to calculate viral titration and multiplicity of infection.Add the calculated volume of Cas9 lentivirus, based on the desired multiplicity of infection, directly to the cells in culture medium containing eight micrograms per milliliter protamine sulfate. Replace the viral medium with fresh medium 16 hours after infection. The next day, apply blasticidin selection by replacing the culture medium with medium containing the previously determined concentration of blasticidin.Continue selection until uninfected cells are eliminated. Validate Cas9 activity with control single guide RNAs for essential genes or use GFP as a reporter to assess knockout efficiency. Calculate the number of successfully infected cells required to reach the desired library coverage using the presented equation.Prepare independent infection mixtures for each biological replicate. After seeding the previously calculated cells, infect them with a determined viral volume in eight milliliters of complete culture media containing eight micrograms per milliliter of protamine sulfate. After 16 hours, replace the viral media with fresh media and incubate the cells at 37 degrees Celsius with 5%carbon dioxide.Apply puromycin selection in the presence of verapamil at the previously determined concentrations to enrich sg-RNA-infected cells in some 159 PT Taxol-resistant cells. Continue selection until all cells in the uninfected control plate are eliminated. Typically, for approximately three days.After completion of puromycin and verapamil selection, trypsinize the cells and collect an initial time point, time zero cell pellet from each biological replicate corresponding to the coverage level decided during transduction. Split the cells into experimental arms, while preserving coverage. One arm as the control with DMSO.And the other as the treatment with Taxol. Maintain the cultures for 14 to 16 population doublings. Calculate the population doubling level using the specified formula.At each passage, monitor cell viability and growth kinetics. Maintain adequate cell numbers to avoid bottlenecks. Upon reaching the desired population doubling level, collect final time point pellets for each replicate and condition while maintaining library coverage per pellet.Store the pellets at 80 degrees Celsius until downstream genomic DNA extraction. Resistant cells continued to proliferate, despite Taxol treatment, while parental cells failed to survive under the same treatment. RNA sequencing identified ABCB1 as one of the most upregulated genes in resistant cells, followed by other ABC transporters, including ABCB4 and ABCA5.The upregulation of ABCB1 expression in resistant cells was validated by quantitative PCR. Taxol-resistant cells showed multi-drug resistance phenotype and resistance to doxorubicin and vincristine. Puromycin selection may not be efficient in resistant cells because puromycin can be pumped out through ABCB1-mediated drug efflux.Verapamil alone did not affect cell viability. However, when combined with chemotherapeutic agents, verapamil restored sensitivity to these agents. Non-toxic doses of verapamil also restored puromycin sensitivity in Taxol-resistant cells.When final time point samples from DMSO or Taxol-treated conditions were compared with the initial time point, almost all essential genes were depleted upon knockout, confirming screen quality. In contrast, the comparison of DMSO-treated and Taxol-treated endpoint samples did not show a depletion of essential genes, as these genes remain essential in both groups. Instead, this comparison reveals Taxol-sensitizing epigenetic modifiers.Multiple positive control genes and epigenetic modifiers were significantly depleted in the EPIKOL screen performed in Taxol-resistant cells. ABCB1-targeting single guide RNAs were specifically depleted in the Taxol-treated group, confirming ABCB1 as a positive control for Taxol sensitivity in the screen. This protocol enables researchers to study therapy resistance in cancer models and identify epigenetic regulators of chemotherapy resistance using CRISPR screening platforms.The most important consideration when screening chemoresistant cells is maintaining library representation and optimizing transaction and selection conditions. The future work will identify therapeutic vulnerabilities of multidrug resistant cancer cells and develop novel combinatorial approaches to overcome chemoresistance.

epigenome-wide-crispr-cas9-based-knockout-screens-on-chemoresistant

Research

JoVE Journal

Cancer Research

This protocol demonstrates how to generate fluorescently marked, genetically defined clonal tumors in the Drosophila eye/antennal imaginal discs (EAD). It describes how to dissect the EAD and brain from the third instar larvae and how to process them to visualize and quantify gene expression changes and tumor invasiveness.

The Drosophila Imaginal Disc Tumor Model: Visualization and Quantification of Gene Expression and Tumor Invasiveness Using Genetic Mosaics

We present a new approach to characterize tumor cells. We combined immunofluorescence with DNA fluorescent-in-situ-hybridization to evaluate cells captured by a functionalized medical wire capable of in vivo enriching CTCs directly from patient blood.

Characterization of Tumor Cells Using a Medical Wire for Capturing Circulating Tumor Cells: A 3D Approach Based on Immunofluorescence and DNA FISH

Currently, immunofluorescent staining on fixed cells is the method of choice for determination of protein expression levels when morphological information is also necessary. This protocol presented herein provides for an alternative method of immunocytochemistry on paraffin-embedded cell blocks.

Determination of Protein Expression Level in Cultured Cells by Immunocytochemistry on Paraffin-embedded Cell Blocks

To expand the ability of laboratories worldwide to assess the eligibility of patients with lung cancer for treatment with pembrolizumab, in a reliable and reproducible manner, we developed an assay that uses the 22C3 antibody concentrate on a widely available immunohistochemical autostainer, for both biopsy and cytology specimens.

Using 22C3 Anti-PD-L1 Antibody Concentrate on Biopsy and Cytology Samples from Non-small Cell Lung Cancer Patients

A HMCA-based imaging plate is presented for invasion assay performance. This plate facilitates the formation of three-dimensional (3D) tumor spheroids and the measurement of cancer cell invasion into the extracellular matrix (ECM). The invasion assay quantification is achieved by semi-automatic analysis.

Analysis of Cancer Cell Invasion and Anti-metastatic Drug Screening Using Hydrogel Micro-chamber Array (HMCA)-based Plates

This manuscript describes a Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) CRISPR-Cas9-based method for simple and expeditious investigation of the role of multiple candidate genes in Acute Myeloid Leukemia (AML) cell proliferation in parallel. This technique is scalable and can be applied in other cancer cell lines as well.

Investigation of Genetic Dependencies Using CRISPR-Cas9-based Competition Assays

Here, we present an easy-to-use co-culture assay to analyze glioblastoma (GBM) migration on patterned neurons. We developed a macro in FiJi software for easy quantification of GBM cell migration on neurons, and observed that neurons modify GBM cell invasive capacity.

Co-culture of Glioblastoma Stem-like Cells on Patterned Neurons to Study Migration and Cellular Interactions

Here we demonstrate a method to apply fluid shear stress to cancer cells in suspension to model the effects of hemodynamic stress on circulating tumor cells.

Modeling the Effects of Hemodynamic Stress on Circulating Tumor Cells using a Syringe and Needle

This protocol describes the isolation of double-negative thymocytes from the mouse thymus followed by retroviral transduction and co-culture on the delta-like 4-expressing bone marrow stromal cell line co-culture system (OP9-DL4) for further functional analysis.

Co-Culture and Transduction of Murine Thymocytes on Delta-Like 4-Expressing Stromal Cells to Study Oncogenes in T-Cell Leukemia

This protocol describes the orthotopic implantation of patient-derived cancer cells in the cecum wall of immunodeficient mice. The model recapitulates advanced colorectal cancer metastatic disease and allows for the evaluation of new therapeutic drugs in a clinically relevant scenario of lung and liver metastases.

Orthotopic Implantation of Patient-Derived Cancer Cells in Mice Recapitulates Advanced Colorectal Cancer

Koç University School of Medicine.  Here, we present a protocol for generating chemoresistant cells using a dose-escalation approach, followed by a CRISPR/Cas9-based screen using a focused sgRNA library targeting epigenetic modifiers to identify regulators of acquired chemoresistance. The protocol also provides multiple optimization points tailored to chemoresistant cell models, offering a robust framework for researchers investigating resistance mechanisms.

Epigenome-Wide CRISPR-Cas9-Based Knockout Screens on Chemoresistant Cells

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