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. 2022 Aug 3;17(8):e0271584. doi: 10.1371/journal.pone.0271584

Single-cell analysis of a high-grade serous ovarian cancer cell line reveals transcriptomic changes and cell subpopulations sensitive to epigenetic combination treatment

Shruthi Sriramkumar 1, Tara X Metcalfe 1, Tim Lai 2,3, Xingyue Zong 1, Fang Fang 4, Heather M O’Hagan 1,4,5,*, Kenneth P Nephew 1,5,6,*
Editor: Lawrence M Pfeffer7
PMCID: PMC9348737  PMID: 35921335

Abstract

Ovarian cancer (OC) is a lethal gynecological malignancy with a five-year survival rate of only 46%. Development of resistance to platinum-based chemotherapy is a common cause of high mortality rates among OC patients. Tumor and transcriptomic heterogeneity are drivers of platinum resistance in OC. Platinum-based chemotherapy enriches for ovarian cancer stem cells (OCSCs) that are chemoresistant and contribute to disease recurrence and relapse. Studies examining the effect of different treatments on subpopulations of HGSOC cell lines are limited. Having previously demonstrated that combined treatment with an enhancer of zeste homolog 2 inhibitor (EZH2i) and a RAC1 GTPase inhibitor (RAC1i) inhibited survival of OCSCs, we investigated EZH2i and RAC1i combination effects on HGSOC heterogeneity using single cell RNA sequencing. We demonstrated that RAC1i reduced expression of stemness and early secretory marker genes, increased expression of an intermediate secretory marker gene and induced inflammatory gene expression. Importantly, RAC1i alone and in combination with EZH2i significantly reduced oxidative phosphorylation and upregulated Sirtuin signaling pathways. Altogether, we demonstrated that combining a RAC1i with an EZH2i promoted differentiation of subpopulations of HGSOC cells, supporting the future development of epigenetic drug combinations as therapeutic approaches in OC.

Introduction

Ovarian cancer (OC) is the fifth leading cause of death among U.S. women [1]. High grade serous OC (HGSOC) accounts for 70% of epithelial ovarian cancer (EOC) [1]. The standard of care for OC patients is debulking surgery followed by platinum-taxane based chemotherapy [2]. Although most patients are initially responsive to chemotherapy, many patients develop tumor recurrence and relapse that rapidly evolves into chemoresistant disease, which is universally fatal [3]. EOC, like most solid tumors, is composed of a diverse array of cell types and it is well established that tumor heterogeneity plays a key role in the progression of EOC [4]. Tumor heterogeneity including transcriptomic heterogeneity also contributes to therapy resistance [5]. Therefore, a better understanding of the tumor subpopulations in general, as well as the effects of treatments on different cell populations in the tumor, is imperative.

In OC, data from preclinical models as well as patient samples have strongly established that platinum-based chemotherapy enriches for drug resistant aldehyde dehydrogenase positive (ALDH+) ovarian cancer stem cells (OCSCs) [6] that contribute to tumor relapse and disease recurrence [7]. We and others have identified several targets that can be exploited to block the platinum driven enrichment of ALDH+ OCSCs [6, 8–13]. In a recent study, we demonstrated that combination treatment of enhancer of zeste homolog 2 inhibitor (EZH2i) and an inhibitor targeting small GTPase RAC1 inhibited the survival of ADLH+ OCSCs in vitro [9]. In addition, we demonstrated that this combination treatment inhibited tumor growth and increased the sensitivity of HGSOC cells to platinum agents in vivo [9]. Although transcriptomic analysis of whole-cell populations showed genes and pathways altered by coadministration of EZH2i and RAC1i [9], it is likely that the treatments also altered both the number of cells and gene expression in subpopulations of cells within the whole-cell population.

Traditional transcriptomic analysis in biomedical research using bulk RNA-seq is state-of-the-art for studying gene expression and trends of whole cell populations. However, to detect biologically meaningful changes in gene expression in subpopulations of cells that make up a small percentage of the total tumor population, single cell RNA-sequencing (scRNA-seq) is currently used. ScRNA-seq is capable of capturing the transcriptomics of each individual cell and provides meaningful insight when used in primary samples and cancer cell lines [14–17]. scRNA-seq has the potential to identify key differences in cellular phenotypes and uncover molecular mechanisms that regulate tumor heterogeneity [18, 19]. For example, using scRNA-seq of primary, relapsed and metastatic OC, CYR61+ cells were identified that can be used as a biomarker of EOC recurrence and could be a therapeutic target [20]. However, to date, scRNA-seq analysis of cell lines representing HGSOC is lacking, and knowledge on baseline transcriptomes and treatment-induced OC cell line heterogeneity at the single cell level is limited.

In the current study, we utilized scRNA-seq to further investigate the effect of combining inhibitors of EZH2 and RAC1 (EZH2i and RACi) on OC cell heterogeneity. With the high resolution of the scRNA-seq transcriptomic analysis, we identified key subpopulations related to OCSCs. We showed that RAC1i treatment either alone or combined with EZH2i significantly increased the number of cells in subpopulations associated with cell differentiation and development and changed gene expression of stemness and secretory markers. Furthermore, the scRNA-seq analysis identified a subpopulation of cells marked by expression of inflammatory genes, which were more uniformly expressed following treatment with RAC1i. This study is the first report of single-cell analysis of a HGSOC cell line and the impact of epigenetic and small GTPase inhibitor combination therapy on HGSOC subpopulations.

Materials and methods

Cell culture

OVCAR3, a representative HGSOC cell line, was maintained at 37°C and 5% CO2 as described previously [6, 9]. This cell line was authenticated by ATCC in 2018. OVCAR3 cells were cultured in DMEM 1X (Gibco, #11995065) containing 10% FBS (R&D Systems, #S11150) without antibiotics. OVCAR3 cells used in the study were passaged for less than 15 passages. 50 mM stock solution of RAC inhibitor (NSC23766, Sigma #553502) was made in DMSO. 5 mM stock solution of EZH2 inhibitor (GSK126, Biovision #2282) was made in DMSO. For all the experiments using these inhibitors, an equivalent amount of DMSO or RAC1i or EZH2i or combination of both was added to cells and incubated for 48 hrs at 37°C and 5% CO2.

Antibodies

Anti-OVGP1 (Abcam, #ab74544), Anti-Ki-67 (Cell Signaling Technology (CST), #9449S), Alexa- Fluor 488 (CST, #4412), Alex-Fluor 594 (CST, #8890).

Single cell RNA-seq

Approximately 10,000 cells per sample were targeted for input to the 10X Genomics Chromium system using the Chromium Next GEM Single Cell 3’ Kit v3.1 at the Indiana University School of Medicine (IUSM) Center for Medical Genomics satellite core in Bloomington. The libraries were sequenced at the IUSM Center for Medical Genomics using a NovaSeq 6000 with a NovaSeq S2 reagent kit v1.0 (100 cycles) with approximately 450 million read pairs per sample. To generate the count matrices, 10X Genomics Cell Ranger (v4.0.0) with default settings and genome assembly, GRCh38 (2020-A) were used. The scripts used for the scRNA-seq analysis are available on Github (https://github.com/timlai4/agnes_scRNA). The resulting matrices were inputted into Seurat (v3.1.5) for further processing, including batch correction using canonical correlation-based alignment analysis. The quality of the data was assessed using various metrics, including the ratio of mitochondrial transcript content to the number of detected genes for each cell. Cells with low gene counts (less than or equal to 1000 genes), high mitochondrial content (greater than 20% mtRNA), or low complexity score (less than or equal to 0.8) were removed, resulting in approximately 7500–10000 remaining cells per sample. Using the built-in Seurat function CellCycleScoring, the cell-cycle stages were determined through analysis of pre-annotated genes. UMI counts of the remaining cells were normalized using SCTransform [21] regressing out cell cycle and mitochondrial effects. Dimension reduction via Principal Component Analysis (PCA) [22] was then performed on the integrated data followed by a Leiden-based clustering method [23] (Wilcoxon rank-sum test; FDR < 0.05 and Log Fold Change > 0.4). To visualize the clusters, we used UMAP [22] dimension reduction to display the points in 2D.

To compare the proportional differences in cell populations between two conditions, we used scProportionTest an R library [14] (https://github.com/rpolicastro/scProportionTest/releases/tag/v1.0.0).

JC-1 staining

1 X 106 OVCAR3 cells were cultured in 100 mm plates for 48 hours. After 48 hours, cells were treated with respective doses of DMSO, 50 μM RAC1i, 5 μM EZH2i or combination of RAC1i and EZH2i for 48 hours. Following the incubation, JC-1 staining was performed as the manufacturer’s instructions. JC-1 (Thermo Fisher, #T3168) stock solutions were made in DMSO at 5 mg/ml concentration. Appropriate amount of JC-1 was added to cells such that the final concentration was 2 μg/ml and the samples were incubated for 30 minutes at 37°C and 5% CO2. After the incubation, cells were collected and analyzed by flow cytometry as described previously [6].

Flow cytometry

LSR II Flow Cytometer (BD Biosciences) was used for the analysis of JC-1stained samples. J-monomers and J-aggregates was measured using 488nm excitation and the signal was detected using AF488 (green) and PE-A (red), respectively.

Immunofluorescence

2 X 105 OVCAR3 cells were cultured in 6-well plates on coverslips and incubated for 48 hours. After 48 hours, cells were treated with EZH2i, RAC1i, combination of EZH2i and RAC1i or an equivalent amount of DMSO for 48 hours and then immunofluorescence was performed. Cells were fixed with 4% paraformaldehyde in PBS. Following fixation, cells were permeabilized with 0.5% Triton-X in PBS and blocked with 1% BSA in PBST (PBS + 0.1% Tween 20). After blocking cells were incubated with anti-OVGP1 (Abcam, #ab74544) and Ki-67 (CST, #9449S) for 1 hour at RT. This was followed by incubation with Alex fluor conjugated secondary antibodies. Prolong Gold Antifade with DAPI (CST, #8961) was used for mounting coverslips.

Imaging

All the images were acquired using the Leica SP8 scanning confocal system with the DMi8-inverted microscope and Leica LASX software (Leica Microsystems). Images were taken using 63X, 1.4NA oil immersion objective at room temperature. Images were processed using Image J (National Institutes of Health, Bethesda, MD).

Results

Single cell RNA-sequencing identifies subpopulations of cells in a HGSOC cell line

To better understand the effects of the RAC1i and EZH2i on subpopulations of HGSOC cells, we performed scRNA-seq using the HGSOC cell line OVCAR3 (Fig 1). Following batch correction and dimension reduction, eleven cell clusters were identified and visualized using UMAP (Fig 1A). For each cluster, the most significant markers across all the samples were generated (see S1 Table for top genes enriched in each cluster). Metascape analysis was performed on marker genes positively associated with the different clusters to identify pathway enrichment [24] (Fig 1B–1E, S2 Table). Metascape analysis revealed that cluster 2 was enriched in genes involved in development and differentiation and cluster 5 was enriched in immune response markers, while cluster 4 and 8 showed enrichment of cell cycle and DNA repair genes (Fig 1B–1D). Interestingly, cluster 6 and 7 showed enrichment of genes involved in mitochondrial oxidative phosphorylation (OXPHOS) (Fig 1E). Clusters 1, 3, 9 and 10 were mostly defined by negative enrichment of certain genes within the given cluster relative to the other clusters and therefore could not be assessed by Metascape analysis. These subpopulations were subsequently assigned as miscellaneous (misc.) clusters.

Fig 1. Single cell analysis identifies cell subpopulations in a HGSOC cell line.

Fig 1

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(A) Uniform Manifold Approximation and Projection for Dimension reduction (UMAP) dot plot of cells from all samples colored by cluster. (B-E) Enriched pathways identified by Metascape analysis of positively associated marker genes in clusters 2 (B), 5 (C), 4 and 8 (D), and 6 and 7 (E).

RAC1 inhibition induces changes in the proportion of cells in different clusters

UMAP visualization revealed that all clusters were present in each treatment group but differed in relative proportion (Fig 2A and 2B, S3 Table). Treatment with 50 μM RAC1i alone induced significant changes in the proportion of cells in clusters 2:Cell differentiation and development (CDD) and 10:Misc 4 compared to DMSO treated cells (Fig 2C). RAC1i and EZH2i (5 μM) combination induced similar changes in cluster 2:CDD and 10:Misc 4 relative to DMSO and decreased the proportion of cells in cluster 9:Misc. 3. However, no significant changes to the proportion of cells in the various clusters were observed after EZH2i treatment alone (Fig 2B). In the CDD cluster, treatment with RAC1i alone or in combination with EZH2i increased the cell proportions from 7% to approximately 25% of the entire cell population while EZH2i alone had no effect on this cluster (Fig 2B), suggesting that RACi treatment altered differentiation.

Fig 2. RAC1i treatment changes the relative proportion of cells in different clusters.

Fig 2

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(A) Individual UMAP dot plots of DMSO, EZH2i (GSK126, 5 μM, 48H), RAC1i (NSC23766, 50 μM, 48H), and Combo (RAC1i + EZH2i) scRNA-seq samples colored by cluster. (B) Relative proportion of cells in each cluster for each sample type. (C) Relative differences in cell proportions for each cluster between RAC1i versus DMSO and combo treatment versus DMSO. Red clusters have an FDR < 0.05 and mean | Log2 fold enrichment | > 1 compared with the normal colon (permutation test; n   10,000).

RAC1 inhibition induces uniform expression of inflammatory marker genes

Cluster 5 was enriched for expression of genes involved in the immune response. Because inflammation and the immune response contribute to EOC, we examined this cluster more closely and identified the genes that were driving this classification, including chemokines, CXCL1, CXCL2, CXCL3, and CXCL8. UMAP blots for these genes indicated that in DMSO and EZH2i only samples, these genes were predominantly only expressed in cluster 5, our identified immune cluster (Fig 3). Following RAC1i treatment alone or in combination with EZH2i, CXCL1 expression increased in cluster 5 but became more apparent in the other clusters as well (Fig 3A). CXCL2, CXCL3, and CXCL8 expression also increased in cluster 5 following RAC1i treatment, with some additional positive cells in the other clusters as well (Fig 3B–3D). The increase in CXCL1, but not CXCL2 or CXCL3, expression was detectable in bulk OVCAR3 cells (S1A Fig). This data suggests that even though RAC1i treatment did not alter the proportion of cells in cluster 5, it did increase expression of marker genes associated with cluster 5 both in that cluster and in other cells in the population.

Fig 3. RAC1i treatment results in broader expression of inflammatory genes.

Fig 3

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(A-D) UMAP dot plots of normalized expression values of CXCL1 (A), CXCL2 (B), CXCL3 (C) and CXCL8 (D) in the indicated sample types.

RAC1i treatment decreases oxidative phosphorylation

As the proportion of cells in cluster 2:CDD after RAC1i and combination treatment markedly increased, it was of interest to identify pathways in this cluster that changed with treatment. To this effect, we compared cluster 2 gene expression in EZH2i, RAC1i and combination treatment to the control. IPA analysis revealed that RAC1i and combination treatment resulted in significant downregulation of oxidative phosphorylation (OXPHOS), cholesterol biosynthesis and estrogen receptor signaling and upregulation of the Sirtuin signaling pathways in cluster 2 when compared to cluster 2 DMSO treated samples (Fig 4A, S4 Table). In contrast, cholesterol biosynthesis was upregulated in cluster 2 following EZH2i treatment alone compared to DMSO (Fig 4A, S4 Table). EIF2 signaling was increased and decreased in EZH2i and RAC1i single treatment samples, respectively, and not altered in the combination treatment cluster 2 cells. To validate the decrease in OXPHOS following RAC1i treatment, we used JC-1, a lipophilic cationic dye, which is commonly used to measure mitochondrial membrane potential (ΔΨM), a surrogate for OXPHOS [25]. In cells with normal ΔΨM and healthy mitochondria, JC-1 will spontaneously aggregate and form red fluorescent J-aggregates; however, in cells with defective ΔΨM, JC-1 does not form aggregates and retains its green fluorescence [26]. Treatment of HGSOC cell line OVCAR3 with RAC1i for 48 hours followed by JC-1 increased green fluorescence intensity (Fig 4B), consistent with our IPA analysis. In the identified Sirtuin signaling pathway enriched following RAC1i treatment, SIRT7 was the most highly upregulated gene. Therefore, we examined SIRT7 expression across the different clusters and treatment groups. Treatment with RAC1i alone or in combination with EZH2i induced SIRT7 expression when compared to treatment with EZH2i alone or DMSO in all cell clusters (Fig 4C). UMAP plots and RT-qPCR of bulk OVCAR3 cells also confirm the induction of SIRT7 in RAC1i only and combination treatment compared to EZH2i treatment alone or DMSO (Fig 4C, S1B Fig).

Fig 4. RAC1 inhibition results in a decrease in oxidative phosphorylation.

Fig 4

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(A) Enriched pathways identified by IPA of gene expression alterations in cluster 2 of the indicated treatment compared to DMSO. Orange or blue bars indicate a positive or negative activation z-score, respectively, as calculated by IPA based on expected directionality of expression changes. (B) Graph of mean green fluorescence intensity of cells untreated (U) or treated with DMSO and RAC1i (50 μM, 48H) followed by JC-1 staining for 30 minutes and analysis by flow cytometry. Graph depicts mean -/+ SEM. N = 3. *P<0.05. (C) Violin and UMAP dot plots of SIRT7 expression levels in the different clusters and sample types.

RAC1 inhibition induces differentiation of OC cells

Previously, we had demonstrated that RAC1i and EZH2i combination treatment reduced the ALDH+ population of OCSCs [9]. Therefore, we were interested in determining if combination treatment had any cell cluster-specific effects on the expression of key genes associated with OCSCs. ALDH1A1 was predominantly expressed in clusters 3–9 and 11 in DMSO treated cells (Fig 5A). CD24 and SOX2 were more uniformly expressed across all clusters in DMSO treated cells. In all clusters in which these genes were expressed, treatment with RAC1i alone or in combination with EZH2i reduced the expression of ALDH1A1, SOX2, CD24 (Fig 5A). Changes in CD24 expression was more variable between clusters than ALDH1A1 and SOX2. Additional genes commonly associated with OCSCs were either expressed at low levels (CD44, PROM1/CD133, ALDH1A2, ALDH1A3) and therefore expression changes with treatment were unable to be determined (S2 Fig). The expression of other expressed ALDH genes (ALDH2, ALDH6A1, ALDH7A1, ALDH9A1) also decreased across most clusters with RAC1i treatment alone or in combination with EZH2i (S2 Fig). Because our initial analysis had identified changes in cluster 2 with RAC1i treatment, which is enriched for genes associated with cell development and differentiation (Fig 2C), we also examined expression of PAX8, an early secretory marker commonly expressed in EOC [27, 28]. PAX8 expression was uniform in control DMSO and EZH2i samples. However, in samples treated with RAC1i alone or in combination, PAX8 expression decreased across all clusters (Fig 5B, top panels). Next, we examined the expression of OVGP1, a marker for intermediate secretory cells reported to be expressed in OC [29, 30]. Expression of OVGP1 in DMSO and EZH2i alone samples was low in all clusters. However, treatment with RAC1i or combination of RAC1i and EZH2i induced expression of OVGP1 across all clusters (Fig 5B, bottom panels). To validate this finding, we treated OVCAR3 cells with EZH2i or RAC1i alone or in combination and performed immunofluorescence for OVGP1 and proliferation marker Ki-67. Consistent with our single cell data, treatment with RAC1i alone or in combination with EZH2i induced the expression of OVGP1 (Fig 5C). OVGP1 stained cells co-stained with Ki-67 indicated that OVGP1 expression was induced in proliferating cells (Fig 5C). Altogether, this data suggests that stemness markers are expressed by most cells in the HGSOC cell line OVCAR3 and that RAC1i alone or in combination caused the cells to become more differentiated, based on reduced expression of stemness markers and increased expression of OVGP1, an intermediate secretory cell marker.

Fig 5. RAC1 inhibition reduces stem cell marker expression in most clusters.

Fig 5

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(A) Violin plots of expression levels of indicated genes in the different clusters and sample types. (B) UMAP dot plots of normalized gene expression values in the indicated sample types. (C) Representative immunofluorescence images of OVGP1 (green) and Ki67 (red) staining after DMSO, EZH2i (5 μM, 48H), RAC1i (50 μM, 48H), or Combo (RAC1i + EZH2i) treatment. N = 3.

Discussion

Single cell transcriptomics provides a powerful means of investigating changes in gene expression at the level of individual cells. While traditional bulk RNA-sequencing gives an overall picture of transcriptomics throughout the entire sample, effects on individual subpopulations can be difficult to infer. In this regard, based on our previous study using bulk RNA-seq showing epigenetic treatment effects on stemness-associated genes and the WNT signaling pathway in HGSOC [9], it was of interest to investigate the possible treatment effects at a single cell resolution. To this end, we used scRNA-seq to profile OVCAR3 cells before and after treatment with EZH2i or RAC1i alone or in combination. We identified cell clusters and treatment effects on individual clusters.

Previous studies have identified several classes of marker genes important for assessing OCSCs [31–33]. In this current study, expression levels of well-known OCSC markers, ALDH1A1, CD24 and SOX2 were used to assess the expression of stemness genes in the clusters. Recently, a study on the fallopian tube epithelium (FTE) using scRNA-seq [34] identified several marker genes of different cell types in the FTE, including secretory cells. PAX8 and OVGP1 were identified as marker genes of early and intermediate secretory cells, respectively. Because the FTE is a major site of origin of HGSOC, we also compared expression levels of the secretory marker genes PAX8 and OVGP1 across our samples. Uniform expression of PAX8 and no OVGP1 expression was seen in all clusters in the control and EZH2i-treated samples, indicative of early secretory cell populations and stemness. Expression levels of ALDH1A1 tended to be more localized yet still prevalent throughout multiple clusters, and RAC1i treatment significantly reduced expression in all clusters. In contrast, expression of OVGP1 was only detectable after treatment with RAC1i. The reciprocal changes in PAX8 and OVGP1 expression suggest that RAC1i treatment is altering secretory cell fates. As a result, we attempted to trace potential cell fates using the other markers in the study [34] but did not observe this phenomenon with any of the other epithelial markers. Nevertheless, these changes can be explored with trajectory analysis in the future.

In the cell differentiation and development cluster 2, we demonstrate that EZH2i and RAC1i combination treatment inhibited the OXPHOS pathway while activating the Sirtuin signaling pathway. The Sirtuin pathway is related to epithelial-to-mesenchymal transition (EMT) suppression and has been studied in lung and ovarian cancers [35, 36]. In particular, the SIRT1 gene represses EMT and antagonizes migration in vitro and metastases in vivo. On the other hand, OXPHOS is important to cell proliferation [37–39]. Studies have shown that CSCs derive energy from OXPHOS and in the presence of active OXPHOS, CSCs develop antioxidant mechanisms to manage ROS [40–44]. In this context, OC cells would no longer respond to ROS-inducing agents, a key shared feature of many current treatment options. As development of chemoresistance is a major obstacle in the treatment of OC patients, determining if the alteration in OXPHOS caused by treatment with RAC1i and EZH2i combination sensitizes HGSOC cells to platinum-based agents is an important question that warrants further investigation.

In a previous study [9], we showed that treatment targeting EZH2 and RAC1 in combination with platinum chemotherapy inhibits OCSCs. Unexpectedly, in our current study, EZH2 mediated effects were not apparent. The precise reason for this is unclear presently. It is possible that the significant effects seen in the bulk transcriptome analysis was an aggregate of incremental changes in multiple clusters, which individually would not be statistically significant. Due to the current limitations of single-cell protocols, there may not have been enough cells to investigate a few of the target pathways, especially the WNT signaling effects. Nevertheless, we detected significant changes after treatment with the RAC1 inhibitor alone or in combination with EZH2i.

Conclusions

Although OC cell lines are widely used to test and validate treatments in vitro and in vivo, to date no single cell analysis studies of HGSOC cell lines have been reported in the literature. As such, the current study represents the first exploratory single-cell analysis of a HGSOC cell line transcriptome and distinguishable subpopulations sensitive to epigenetic combination treatment. Although most of the clusters were distinguished through their biological functions, gene signatures were identified that we believe will drive future research on targeted therapy, including the OXPHOS and Sirtuin pathways.

Supporting information

S1 Fig. RAC1 inhibition induces altered expression of some genes in bulk OVCAR3 cells.

A and B) OVCAR3 cells were treated with DMSO, 5 μM. EZH2i, 50 μM RAC1i or combination for 48 hours. cDNA was made from RNA collected from bulk populations of cells and RT-qPCR was performed. Expression of the indicated gene was normalized to a housekeeping gene and then to the untreated control. N = 3. Graphs depict mean +/- SEM. *P<0.05, **P<0.01, ***P<0.001.

(DOCX)

Click here for additional data file. (161.7KB, docx)
S2 Fig. RAC1 inhibition reduces expression of several ALDH isoforms in most clusters.

Violin plots of expression levels of indicated genes in the different clusters and sample types.

(DOCX)

Click here for additional data file. (418.7KB, docx)
S1 Table. Top genes enriched in each cluster.

(DOCX)

Click here for additional data file. (14.8KB, docx)
S2 Table. Metascape analysis results using genes enriched in each cluster.

(XLSX)

Click here for additional data file. (82.4KB, xlsx)
S3 Table. Number of cells in each cluster for each treatment group.

(DOCX)

Click here for additional data file. (15.9KB, docx)
S4 Table. IPA analysis of differentially expressed genes in cluster 2 in the indicated comparisons.

(XLSX)

Click here for additional data file. (32.6KB, xlsx)

Acknowledgments

We thank the Indiana University Flow Cytometry Core Facility and Center for Genomics and Bioinformatics for their assistance.

Data Availability

Single-cell RNA-seq data generated in this study are available through NCBI’s Gene Expression Omnibus (GEO) through GEO series accession number GSE207993.

Funding Statement

This research was funded in part by the Ovarian Cancer Research Alliance (grant number 458788 to HMOH and KPN), the Ovarian Cancer Alliance of Greater Cincinnati (to KPN) and through the IU Simon Comprehensive Cancer Center P30 Support Grant (P30CA082709-20). SS was supported by the Doane and Eunice Dahl Wright Fellowship generously provided by Ms. Imogen Dahl. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Lawrence M Pfeffer

12 May 2022

PONE-D-22-09003Single-cell Analysis of a High-grade Serous Ovarian Cancer Cell Line Reveals Transcriptomic Changes and Cell Subpopulations Sensitive to Epigenetic Combination TreatmentPLOS ONE

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Reviewer #1: Partly

Reviewer #2: Partly

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Reviewer #2: I Don't Know

**********

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #2: Yes

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Reviewer #1: Overview

Nephew, et al., present data on RNA expression in single cells from the Ovcar3 cell line comparing untreated cells to cells treated with inhibitors of EZH2 and RAC1. This work is a follow up to their previous publication that identified DAB2IP as a tumor suppressor that blocks ovarian cancer stem cells, based on cell line models.

Interesting findings reported include

No major changes in gene expression after treating with the EZH2 inhibitor for 48 hours (Fig 2B). This is quite surprising, as one might expect large scale gene expression changes after blocking this histone methyltransferase, which would then cause differential clustering. (eg, Tiffen, et al., Oncotarget 2015)

RAC1 inhibition has remarkable effects causing upregulation of chemokines, SIRT77 and OVGP1, while downregulating ox/phos genes, Pax8 and stem cell markers ADLH1A1, CD24 and SOX2 in OVCAR3 cell line.

The findings are interesting, although their implications for ovarian cancer are tenuous until they are confirmed in more cell lines and in primary ovarian cancer tissue.

Methods summary

Authors treated the Ovcar3 cell line for 48 hrs with DMSO (control), NSC23766 (RAC1 inhibitor), GSK126 (EZH2 inhibitor), or both inhibitors together and then performed single cell RNAseq (scRNAseq) using the 10X Genomics platform. Data was analyzed using CellRanger and Seurat R packages. Data from ~7 to 10k cells from each treatment were combined and analyzed together. Cells were also treated with JC-1 and analyzed by IFC. The Ovcar3 cell line is a hypotriploid, poorly differentiated papillary adenocarcinoma derived from the ascites of a patient who had been treated with cyclophosphamide, adriamycin, and cisplatin 8 months prior to cell collection in 1982 (Hamilton, et al., Cancer Research 1983).

Major concerns

Cluster numbers

Leiden based clustering implemented by Seurat is strongly affected by selection of the following four input parameters: number of PCs, k-value, prune value and resolution value. Altering these required input parameters affects the number of clusters identified. Authors should perform Leiden based clustering multiple times using a range of these parameters and demonstrate that the clustering solution of 12, which is extensively analyzed in the paper, is the most robust clustering solution. Without this assurance, it is difficult to justify the findings.

Effect of inhibiting RAC1 on cell differentiation conclusion

In figure 1B authors list GO terms and pathways significantly associated with upregulated genes from cluster 2. The second highest associated GO term, based on p-value, is "negative regulation of cell differentiation". In figure 2B, authors show that cluster 2 is enriched in cells treated with RAC1 inhibitor. This would suggest that RAC1i treatment increases "negative regulation of cell differentiation", and yet authors conclude that "…treatment with RAC1i alone or in combination with EZH2i increased the cell proportions from 7% to approximately 25% of the entire cell population while EZH2i alone had no effect on this cluster (Figure 2B), suggesting that RACi treatment induced differentiation." This conclusion seems to be contradictory to their data. Authors should explain how their data indicates RAC1i induces differentiation and does not negatively regulate cell differentiation, as cluster 2 is enriched in negative regulators of differentiation.

Minor concerns

Cell numbers

Fig 2B shows changes in the percentages of cells in each cluster based on treatment. The statistics indicate that RAC1 treatment statistically increases the proportion of cells in cluster 2 and reduces the proportion in cluster 4. As these numbers are based on relative proportion, it is important to know if there was a large difference in growth of the cell populations after treatment with the inhibitors. One assumes that the authors submitted equal numbers of viable cells for each condition for sequencing, but it should be reported how much the treatment affected viability and growth.

Line 263 describes cluster 2 as "…which is enriched for genes associated with cell death and differentiation (Figure 2C)". This is possibly a typo, as cluster 2 did not have any cell death pathways associated (Fig 1B). I think authors meant to say, "cell development and differentiation".

The exact number of cells depicted in Fig 2A in each panel should be listed somewhere in the paper, so it is known how many cells were analyzed in each condition.

The authors conclusion that RAC1 inhibition reduces numbers of ovarian cancer stem cells by analyzing more stem cell markers and presenting these findings (eg PROM1/CD133, cKIT/CD117, CD44, the other ALDH genes)

Reviewer #2: To draw a valid conclusion, it is necessary to provide more detailed scRNA-seq data and additional results of validation experiments as specified below.

1. For each cluster of cells in scRNA-seq analysis, the most significant markers across all the samples should be tabulated and presented.

2. Genes contributed to the enriched pathways in each cell cluster (Figure 1B-E) should be tabulated and presented.

3. Validation experiments should be conducted to confirm that RAC1i treatment induces expression of inflammatory genes in OVCAR3 cells. It will be interesting to examine whether RACi-induced expression of CXCL1-3 and 8 can be replicated using other OV cell lines.

4. Genes contributed to the altered pathways by EZH2i and RAC1i in Cluster 2 (Figure 4) should be tabulated and presented. Validation experiments should be conducted to confirm that RAC1i treatment induces SIRT7 expression in OVCAR3 cells and examine whether this effect can be extended to other OV cell lines.

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2022 Aug 3;17(8):e0271584. doi: 10.1371/journal.pone.0271584.r002

Author response to Decision Letter 0

29 Jun 2022

Reviewer #1:

Overview

Nephew, et al., present data on RNA expression in single cells from the Ovcar3 cell line comparing untreated cells to cells treated with inhibitors of EZH2 and RAC1. This work is a follow up to their previous publication that identified DAB2IP as a tumor suppressor that blocks ovarian cancer stem cells, based on cell line models.

Interesting findings reported include

No major changes in gene expression after treating with the EZH2 inhibitor for 48 hours (Fig 2B). This is quite surprising, as one might expect large scale gene expression changes after blocking this histone methyltransferase, which would then cause differential clustering. (eg, Tiffen, et al., Oncotarget 2015)

RAC1 inhibition has remarkable effects causing upregulation of chemokines, SIRT77 and OVGP1, while downregulating ox/phos genes, Pax8 and stem cell markers ADLH1A1, CD24 and SOX2 in OVCAR3 cell line.

The findings are interesting, although their implications for ovarian cancer are tenuous until they are confirmed in more cell lines and in primary ovarian cancer tissue.

Methods summary

Authors treated the Ovcar3 cell line for 48 hrs with DMSO (control), NSC23766 (RAC1 inhibitor), GSK126 (EZH2 inhibitor), or both inhibitors together and then performed single cell RNAseq (scRNAseq) using the 10X Genomics platform. Data was analyzed using CellRanger and Seurat R packages. Data from ~7 to 10k cells from each treatment were combined and analyzed together. Cells were also treated with JC-1 and analyzed by IFC. The Ovcar3 cell line is a hypotriploid, poorly differentiated papillary adenocarcinoma derived from the ascites of a patient who had been treated with cyclophosphamide, adriamycin, and cisplatin 8 months prior to cell collection in 1982 (Hamilton, et al., Cancer Research 1983).

Major concerns

1. Cluster numbers

Leiden based clustering implemented by Seurat is strongly affected by selection of the following four input parameters: number of PCs, k-value, prune value and resolution value. Altering these required input parameters affects the number of clusters identified. Authors should perform Leiden based clustering multiple times using a range of these parameters and demonstrate that the clustering solution of 12, which is extensively analyzed in the paper, is the most robust clustering solution. Without this assurance, it is difficult to justify the findings.

Response: Thank you to the reviewer for this comment. We agree that there are many parameters that affect clustering. We performed a grid search on the clustering parameters (Schneider I. et al. Journal of Translational Genetics and Genomics 2021) and also determined silhouette scores to quantify the performance of our clustering (Rousseeuw PJ. Mathematics 1987). Based on the silhouette plot (Rebuttal Figure 1), for most of our labelled clusters, most cells had positive scores, which indicated better clustering. In general, the means of the silhouette scores for the clusters were also around or above the mean of scores for the clusters using the highest grid search parameters. However, cluster 5: immune/inflammation related did not perform well even though the CXCL marker genes for this cluster were robust (Figure 3). There is not a standard method for analyzing scRNA-seq data and the results we found were statistically significant so even if the clustering was imperfect, the results are still sound and verified in many cases by additional experiments. Furthermore, many of the updated packages and bioinformatics solutions to optimize the parameters for clustering were not available at the time we performed the initial analysis.

Rebuttal Figure 1. Silhouette scores for clustering strategy used in the manuscript.

2. Effect of inhibiting RAC1 on cell differentiation conclusion

In figure 1B authors list GO terms and pathways significantly associated with upregulated genes from cluster 2. The second highest associated GO term, based on p-value, is "negative regulation of cell differentiation". In figure 2B, authors show that cluster 2 is enriched in cells treated with RAC1 inhibitor. This would suggest that RAC1i treatment increases "negative regulation of cell differentiation", and yet authors conclude that "…treatment with RAC1i alone or in combination with EZH2i increased the cell proportions from 7% to approximately 25% of the entire cell population while EZH2i alone had no effect on this cluster (Figure 2B), suggesting that RACi treatment induced differentiation." This conclusion seems to be contradictory to their data. Authors should explain how their data indicates RAC1i induces differentiation and does not negatively regulate cell differentiation, as cluster 2 is enriched in negative regulators of differentiation.

Response: Thank you to the reviewer for pointing out this potentially confusing point. Regarding the GO analysis, more than 50% of genes used for enrichment in “negative regulation of cell differentiation” are also in the GO term “positive regulation of cell differentiation”. Therefore, while the GO analysis suggests that cluster 2 is enriched for regulation of cell differentiation, the directionality is difficult to determine from the GO analysis alone. In Figure 5, we analyzed the expression of genes associated with OCSCs in our scRNA-seq data in the different treatment groups and performed immunofluorescence for OVGP1, which increases in expression in more differentiated OC cells. These results suggest that RAC1 inhibition increased cell differentiation. Because in Figure 2 we do not yet know the directionality of the change in differentiation, in the revised manuscript we have changed the statement mentioned by the reviewer to “... suggesting that RACi treatment altered differentiation”.

Minor concerns

1. Cell numbers

Fig 2B shows changes in the percentages of cells in each cluster based on treatment. The statistics indicate that RAC1 treatment statistically increases the proportion of cells in cluster 2 and reduces the proportion in cluster 4. As these numbers are based on relative proportion, it is important to know if there was a large difference in growth of the cell populations after treatment with the inhibitors. One assumes that the authors submitted equal numbers of viable cells for each condition for sequencing, but it should be reported how much the treatment affected viability and growth.

Response: As the reviewer suggests, we submitted equal numbers of viable cells for each condition for 10X Chromium single cell processing and library preparation and similar numbers of cells were sequenced for each condition. The number of cells in each treatment group was as follows: DMSO 7530, EZH2i 7651, RAC1i 10207, Combo 8232 (these numbers are now included in Supplementary Table S3). In our previous publication (Zong X, et al. Cancer Research 2020), we determined that 50 μM RAC1i had minimal effect on tumorsphere survival/growth when used alone suggesting that the changes in percentages of cells in clusters 2 and 4 are caused by RAC1i treatment and not by decreased cell viability.

2. Line 263 describes cluster 2 as "…which is enriched for genes associated with cell death and differentiation (Figure 2C)". This is possibly a typo, as cluster 2 did not have any cell death pathways associated (Fig 1B). I think authors meant to say, "cell development and differentiation".

Response: Thank you to the reviewer for pointing out this typo. We have changed the text as suggested.

3. The exact number of cells depicted in Fig 2A in each panel should be listed somewhere in the paper, so it is known how many cells were analyzed in each condition.

Response: We have now included the number of cells depicted in Figure 2A in Supplementary Table S3.

4. The authors conclusion that RAC1 inhibition reduces numbers of ovarian cancer stem cells by analyzing more stem cell markers and presenting these findings (eg PROM1/CD133, cKIT/CD117, CD44, the other ALDH genes)

Response: Thank you to the reviewer for this suggestion. We have now included violin plots for CD44, PROM1/CD133, ALDH1A2, ALDH1A3 in Supplementary Figure 1. These genes were expressed at low levels in all conditions and cKIT/CD117 was not detectable so changes in expression of these genes with treatment could not be determined. We also included plots for additional expressed ALDH isoforms. The expression of these genes decreased across most clusters with RAC1i treatment alone or in combination with EZH2i.

Reviewer #2:

To draw a valid conclusion, it is necessary to provide more detailed scRNA-seq data and additional results of validation experiments as specified below.

1. For each cluster of cells in scRNA-seq analysis, the most significant markers across all the samples should be tabulated and presented.

Response: Thank you to the reviewer for this suggestion. We have now included the most significant marker genes for each cluster in Supplementary Table S1. When there were greater than 10 genes enriched in a given cluster only the top 10 were included.

2. Genes contributed to the enriched pathways in each cell cluster (Figure 1B-E) should be tabulated and presented.

Response: As requested, in the revised manuscript we have included Supplementary Table S2, which includes lists of the genes that contributed to the enriched pathways in each cluster.

3. Validation experiments should be conducted to confirm that RAC1i treatment induces expression of inflammatory genes in OVCAR3 cells. It will be interesting to examine whether RACi-induced expression of CXCL1-3 and 8 can be replicated using other OV cell lines.

Response: In the revised manuscript, we now include expression data for CXCL1-3 in bulk populations of OVCAR3 cells (Supplementary Figure S1A). CXCL1 expression increased with RAC1 inhibition but changes in CXCL2 and CXCL3 expression were not detectable in the bulk population. These findings are consistent with the increase in CXCL1 expression following RAC1i occurring in many clusters whereas CXCL2, CXCL3, and CXCL8 only had increased expression in a small subpopulation of cells (Figure 3). The results also emphasize the importance of performing scRNA-seq, which can identify important changes in gene expression that occur in only a small proportion of the total population of cells. We have not assayed RAC1i-induced changes in CXCL gene expression in other ovarian cancer cell lines as we would be assaying changes in bulk gene expression, which may not reflect changes at the single cell level.

4. Genes contributed to the altered pathways by EZH2i and RAC1i in Cluster 2 (Figure 4) should be tabulated and presented. Validation experiments should be conducted to confirm that RAC1i treatment induces SIRT7 expression in OVCAR3 cells and examine whether this effect can be extended to other OV cell lines.

Response: As requested, we have now included Supplementary Table S4, which lists the genes in cluster 2 that contributed to the pathway enrichment in IPA analysis in Figure 4. We have also confirmed that RAC1i treatment induces SIRT7 expression in bulk OVCAR3 cells (Supplementary Figure S1B). We have not assayed RAC1i-induced changes in SIRT7 gene expression in other ovarian cancer cell lines as we would be assaying changes in bulk gene expression, which may not reflect changes at the single cell level.

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Decision Letter 1

Lawrence M Pfeffer

4 Jul 2022

Single-cell Analysis of a High-grade Serous Ovarian Cancer Cell Line Reveals Transcriptomic Changes and Cell Subpopulations Sensitive to Epigenetic Combination Treatment

PONE-D-22-09003R1

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The authors have been highly responsive to the reviewers and the manuscript is markedly improved. It now merits publication in PLOS One

Reviewers' comments:

Acceptance letter

Lawrence M Pfeffer

25 Jul 2022

PONE-D-22-09003R1

Single-cell Analysis of a High-grade Serous Ovarian Cancer Cell Line Reveals Transcriptomic Changes and Cell Subpopulations Sensitive to Epigenetic Combination Treatment

Dear Dr. Nephew:

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. RAC1 inhibition induces altered expression of some genes in bulk OVCAR3 cells.

    A and B) OVCAR3 cells were treated with DMSO, 5 μM. EZH2i, 50 μM RAC1i or combination for 48 hours. cDNA was made from RNA collected from bulk populations of cells and RT-qPCR was performed. Expression of the indicated gene was normalized to a housekeeping gene and then to the untreated control. N = 3. Graphs depict mean +/- SEM. *P<0.05, **P<0.01, ***P<0.001.

    (DOCX)

    Click here for additional data file. (161.7KB, docx)
    S2 Fig. RAC1 inhibition reduces expression of several ALDH isoforms in most clusters.

    Violin plots of expression levels of indicated genes in the different clusters and sample types.

    (DOCX)

    Click here for additional data file. (418.7KB, docx)
    S1 Table. Top genes enriched in each cluster.

    (DOCX)

    Click here for additional data file. (14.8KB, docx)
    S2 Table. Metascape analysis results using genes enriched in each cluster.

    (XLSX)

    Click here for additional data file. (82.4KB, xlsx)
    S3 Table. Number of cells in each cluster for each treatment group.

    (DOCX)

    Click here for additional data file. (15.9KB, docx)
    S4 Table. IPA analysis of differentially expressed genes in cluster 2 in the indicated comparisons.

    (XLSX)

    Click here for additional data file. (32.6KB, xlsx)
    Attachment

    Submitted filename: Response to Reviewers.docx

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    Data Availability Statement

    Single-cell RNA-seq data generated in this study are available through NCBI’s Gene Expression Omnibus (GEO) through GEO series accession number GSE207993.

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