1. Biochemistry and Chemical Biology
  2. Cell Biology

Cell size modulates ferroptosis susceptibility

  1. Evgeny Zatulovskiy  Is a corresponding author
  2. Magdalena B Murray
  3. Shuyuan Zhang
  4. Scott J Dixon
  5. Jan M Skotheim  Is a corresponding author
  1. Department of Biochemistry, University of Cambridge, United Kingdom
  2. Department of Biology, Stanford University, United States
  3. Chan Zuckerberg Biohub San Francisco, United States
https://doi.org/10.7554/eLife.111544.2
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  1. Evgeny Zatulovskiy
  2. Magdalena B Murray
  3. Shuyuan Zhang
  4. Scott J Dixon
  5. Jan M Skotheim
(2026)
Cell size modulates ferroptosis susceptibility
eLife 15:RP111544.
https://doi.org/10.7554/eLife.111544.2
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eLife Assessment

This important study highlights how cell size influences various cellular responses, with a particular focus on ferroptosis. The evidence presented is convincing, employing multiple model systems and experimental approaches to support the conclusions. This work will be of significant interest to the fields of cell size, ferroptosis, and cancer biology.

[Editors' note: this paper was reviewed by Review Commons.]

https://doi.org/10.7554/eLife.111544.2.sa0

Abstract

Size is a fundamental property of cells that influences many aspects of their physiology. This is because cell size sets the scale for all subcellular components and drives changes in the composition of the proteome. Given that large and small cells differ in their biochemical composition, we hypothesized that they should also differ in how they respond to signals and make decisions. Here, we investigated how cell size affects the susceptibility of human cells to cell death. We found that large cells are more resistant to ferroptosis caused by system xc- inhibition. Ferroptosis is a type of cell death characterized by the iron-dependent accumulation of toxic lipid peroxides. This process is opposed by cysteine-dependent lipid peroxide detoxification mechanisms. We found that larger cells exhibit higher concentrations of the cysteine-containing metabolite glutathione and lower concentrations of membrane lipid peroxides. Mechanistically, this can be explained by the fact that larger cells had lower concentrations of an enzyme that enriches cellular membranes with peroxidation-prone polyunsaturated fatty acids, ACSL4, and increased concentrations of the glutathione-producing enzymes glutamate-cysteine ligase and glutathione synthetase, the iron-chelating protein ferritin, and the lysosomal protease cathepsin B, which can catabolize cysteine-rich extracellular proteins to produce additional cystine for fueling the synthesis of glutathione. Taken together, our results highlight the significant impact of cell size on cellular function and survival, revealing a size-dependent vulnerability to ferroptosis that could influence therapeutic strategies based on this cell death pathway.

Introduction

Cell size is fundamental to cell physiology. This is because it impacts cell geometry and sets the scale of all biosynthetic processes in the cell (Chan and Marshall, 2010; Ginzberg et al., 2015; Zatulovskiy and Skotheim, 2020). The importance of cell size is most clearly seen when yeast and mammalian cells become excessively large. In yeast and mammalian cells, disproportionate increases in cytoplasmic volume relative to genome content reduce the effective concentration of DNA-encoded templates and their products, leading to impaired maintenance of protein and mRNA concentrations and reduced growth capacity (Neurohr et al., 2019; Lanz et al., 2022; Lanz et al., 2024; Zatulovskiy et al., 2022). This defect can be buffered when increases in cell size are balanced by increased ploidy, emphasizing that the relevant parameter is not cell size alone but the ratio between genome content and cytoplasmic volume. Consistent with this idea, excessively large cells with dilute genomes exhibit features of senescence and lose the ability to enter the cell division cycle. Cell size regulation is also compromised in disease and aging (Sandlin et al., 2022; Nguyen et al., 2016; Lengefeld et al., 2021). As animals, including humans, grow older, they contain more ‘senescent’ cells that tend to be larger (Davies et al., 2022). For example, basal keratinocytes are about 1.5 times as large when humans are 80 years old versus 20 (Liao et al., 2013). Conversely, cancer cells often exhibit an extreme heterogeneity in cell size, which may be associated with more proliferation (Sandlin et al., 2022; Nguyen et al., 2016; Li et al., 2015; Bell and Waizbard, 1986). Taken together, these studies argue for the importance of cell size in both natural and disease contexts.

Due to the importance of cell size for cell physiology, this property is highly regulated by distinct classes of molecular mechanisms. One class of mechanisms regulates the cell growth rate so that smaller cells grow faster and add proportionally more biomass than larger cells within one cell division cycle to ensure that all cells divide at similar sizes (Ginzberg et al., 2018; Liu et al., 2024; Conlon and Raff, 2003). A second class of molecular mechanisms, useful in more regular shaped cell types like rod shaped bacteria and fission yeast, relies on geometrical parameters, such as cell length or surface area to measure cell size and determine the appropriate timing for initiating cell division (Miller et al., 2023; Martin and Berthelot-Grosjean, 2009; Pan et al., 2014; Bhatia et al., 2014; Moseley and Nurse, 2010). Finally, a third class of mechanisms rely on differential changes in protein concentrations of cell cycle activators and cell cycle inhibitors as cells grow larger (Chen et al., 2020b; Zatulovskiy et al., 2020; Schmoller et al., 2015). Typically, cell cycle activators either remain at constant concentration or increase with cell size, while some key cell cycle inhibitors, like the transcriptional inhibitors Whi5 in budding yeast and the retinoblastoma protein RB in human cells, are progressively diluted in larger cells to trigger their division (Zatulovskiy et al., 2020; Schmoller et al., 2015; Zhang et al., 2022b; Zhang et al., 2024).

That some larger cells initiate division through increases in the concentrations of cell cycle activators relative to cell cycle inhibitors was surprising because previous bulk studies showed that total protein concentration remained constant as cells grew larger. However, this constant concentration of total protein masked changes in the concentrations of individual proteins as cells grow larger, as revealed by a systematic quantitative proteomics analysis (Lanz et al., 2022; Lanz et al., 2024; Zatulovskiy et al., 2022). The proteomics analyses revealed that as cell size increases, the concentrations of numerous senescence-related proteins begin to approach those of a senescent cell, even before cells lose the ability to enter the cell division cycle (Lanz et al., 2022; Crozier et al., 2023). This may in part explain the association of large cell size with the senescent state. Moreover, large cells have difficulty in replicating and repairing their genomes, which may be related to the lower concentrations of DNA replication and repair enzymes observed in larger cells (Lanz et al., 2022; Crozier et al., 2022; Manohar et al., 2023; Foy et al., 2023; Wilson et al., 2023). Taken together, these results indicate that a cell size increase in the absence of DNA replication impacts cell physiology through changing the composition of the proteome.

If cell size drives widespread changes in proteome composition, then this might impact many areas of cell physiology, including the susceptibility to cell death. Indeed, proteomic and transcriptomic comparisons of different-sized cells within the same cell type identified concentration changes for many genes involved in regulated cell death (Lanz et al., 2022; Miettinen and Björklund, 2016). Consistent with this, cell size is correlated with some cell death decisions – smaller cells have a higher likelihood to undergo apoptosis both in cell cultures and during C. elegans and D. melanogaster development (Miettinen and Björklund, 2016; Sethi et al., 2022; Kiyomitsu and Cheeseman, 2013; Hatzold and Conradt, 2008; Chen et al., 2016; Cordes et al., 2006). Inspired by these studies, we aimed to mechanistically investigate how cell size affects different cell death responses in human cells, as it remains unknown whether cell size impacts non-apoptotic cell death.

To test if there is a relationship between cell size and cell death, we used a high-throughput microscopy-based approach to examine a variety of stresses (Forcina et al., 2017; Inde et al., 2020). We found that susceptibility to ferroptosis, an iron-dependent form of cell death, demonstrated the strongest dependence on cell size. This finding resonates with a recent report that cell susceptibility to RSL3, a chemical that induces ferroptosis by inhibiting the reduction of lipid peroxides by glutathione peroxidase 4 (GPX4), increases with cell size (Chan et al., 2025). Inhibition of the cystine/glutamate antiporter system xc-, using the small molecule erastin2 (Era2) (Dixon et al., 2014), preferentially induced cell death in smaller cells, while larger cells were more resistant. Ferroptosis is characterized by the accumulation of toxic lipid peroxides (Jiang et al., 2021; Dixon and Olzmann, 2024). To suppress this accumulation and prevent ferroptosis, cells can use cystine/cysteine-derived metabolites, like the reduced tripeptide glutathione (GSH), to prevent the accumulation of peroxidized lipids. The lower susceptibility of large cells to Era2-induced ferroptosis can likely be explained by our observation that larger cells are able to produce higher amounts of GSH per unit protein mass, and that they have a lower concentration of enzymes like ACSL4 that incorporate oxidizable polyunsaturated fatty acids into the plasma membrane (Doll et al., 2017). Supporting this model, the genetic disruption of ACSL4 reduced the size dependence of lipid peroxidation and ferroptotic cell death. Taken together, our results show how large cell size can protect against ferroptosis.

Results

Larger cells are less susceptible to ferroptosis

To test our hypothesis that cell size affects susceptibility to cell death, we exposed cells of different sizes to a variety of compounds known to induce different forms of cell death. To do this, we first used fluorescence-activated cell sorting (FACS) to isolate small and large G1-phase HMEC-hTERT cells (human mammary epithelium cells immortalized with telomerase, herein referred to as HMEC for brevity) (Figure 1A and B; Lanz et al., 2022). We sorted cells for size using side scatter, which correlates well with cell size (Lanz et al., 2022; Tzur et al., 2011; Berenson et al., 2019). In this experiment, we used DNA staining with Hoechst dye to collect only the cells that were in G1 phase of the cell cycle, since the cell cycle phase can influence cell survival (Rodencal et al., 2024; Ruiz-Losada et al., 2022; Lee et al., 2024; Kuganesan et al., 2023). The small- and large-sorted cells were then seeded into 384-well plates and treated with death-inducing compounds over a range of different doses. Each population of cells was then monitored using high-throughput fluorescence microscopy for 72 hr to measure dose-dependent responses and cell death kinetics (Forcina et al., 2017; Inde et al., 2020).

Figure 1
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High-throughput microscopy-based measurement of cell death susceptibility in different-sized cells.

(A) Experiment schematic: a genetic construct encoding nuclear-localized fluorescent protein mKate2 was delivered into HMEC cells using lentiviral transduction. After selection, these cells were sorted using fluorescence-activated cell sorting (FACS) by cell cycle phase and cell size into small and large G1 cell bins (smallest and largest 5% of G1 cells). The FACS-sorted cells were seeded on 384-well plates and allowed to settle overnight before being treated with death-inducing compounds in the media containing a dead-cell dye SYTOX Green. The cells were imaged for 72 hr during treatment, and live cells were identified by the presence of nuclear mKate2 fluorescence, while dead cells were identified by the presence of the SYTOX Green signal (Forcina et al., 2017; Inde et al., 2020). The number of live and dead cells were automatically tracked over time to determine the dose-response curves and cell death kinetics. (B) Cell size distributions of small and large G1-phase HMEC cells after FACS sorting, measured on a Coulter counter. (C) Dose-response curves of small and large FACS-sorted G1 HMEC cells treated with a potent ferroptosis-inducing compound erastin2 (Era2). Rep1 and rep2 denote two different biological replicates, each with two technical replicates. (D) Cell death kinetics of small and large FACS-sorted G1 HMEC cells treated with 10 µM Era2. Dots show the means of two biological replicates and error bars denote the range. (E) Dose-response curves of small and large FACS-sorted G1 HMEC cells treated for 72 hr with other lethal compounds: puromycin, doxorubicin, anisomycin, tunicamycin, and bortezomib. Each experiment was performed twice independently, and each biological replicate included two technical replicates. The dots show averages of two biological replicates.

Comparing dose-response curves for small and large cells across different compounds, we observed the largest size-dependent differences in cell death susceptibility in response to erastin2 (Era2). Era2 induces ferroptosis by inhibiting system xc- (Dixon et al., 2014). Larger HMEC cells were resistant to higher doses of Era2 than smaller cells. For example, at the 72 hr time point, the Era2 IC50 was 28±11 µM (mean ± SD) for large cells versus 2.0±1.4 µM for small cells (Student’s t-test: p=0.039) (Figure 1C). Moreover, larger cells exhibit slower cell death kinetics (Figure 1D). By contrast, for compounds that cause cell death by disrupting protein synthesis (puromycin, anisomycin), inducing DNA damage (doxorubicin), inhibiting protein glycosylation (tunicamycin), or blocking proteasome function (bortezomib), IC50 values were not statistically significantly different for large versus small cells (Figure 1E). We note that this does not eliminate the possibility that there are size-dependent differences in susceptibilities to these compounds that would manifest if our size range were expanded further. However, since the most pronounced differences were observed for Era2, we focused our investigation on how cell size affects ferroptosis.

To test if protection against ferroptosis by large cell size was generalizable beyond HMEC cells isolated by FACS, we used alternative methods to generate different-sized populations of cells and tested additional cell lines. We examined the HT-1080 fibrosarcoma cell line, commonly used in ferroptosis studies (Dixon et al., 2012), and non-transformed telomerase-immortalized retinal pigment epithelium cells (RPE-1 cell line), commonly used in cell cycle and cell size studies. We used FACS to sort G1-phase HT-1080 cells into four cell size bins and treated these cells with 0.3 µM Era2. As observed in HMEC cells, larger HT-1080 and RPE-1 cells were more resistant to ferroptotic cell death (Figure 2—figure supplement 1A). Next, to generate populations of different-sized cells without cell sorting, we arrested HT-1080 cells in early G1 using the cell-cycle inhibitor palbociclib (CDK4/6 inhibitor) for 2–6 days (Neurohr et al., 2019; Lanz et al., 2022; Crozier et al., 2023; Manohar et al., 2023; Foy et al., 2023). Larger cells, which were arrested in G1 for a longer time, were less sensitive to Era2 than smaller cells arrested for a shorter duration (Figure 2A and B). We note that non-arrested cells had a lower susceptibility to Era2-induced ferroptosis compared to cells that were arrested in G1 for 2–3 days, despite being smaller in size. This is likely due to the difference in the fraction of cells in different cell cycle phases between arrested and non-arrested conditions, since cells in S/G2/M phases are known to be more resistant to ferroptosis than cells in G0/G1 phases (Rodencal et al., 2024; Kuganesan et al., 2023).

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Larger cells are less susceptible to Era2-induced ferroptosis.

(A) Cumulative cell size distributions of HT-1080 cells, whose cell cycle was arrested in G1 phase for 0, 2, 3, 5, or 6 days with 1 µM of the CDK4/6 inhibitor palbociclib. (B) Cell survival percentage of HT-1080 cells whose size was increased through palbociclib-induced cell cycle arrest. After pre-treatment with palbociclib for the indicated number of days, the cells were exposed to 0.4 µM Era2 in the presence of palbociclib for 24 hr. Cell survival percentage was calculated relative to the cells treated only with palbociclib. (C) Cell cycle phase distribution of asynchronously proliferating retinal pigment epithelium (RPE-1) cells, and RPE-1 cells treated with the CDK4/6 inhibitor palbociclib for 2 or 4 days. (D) Cell size distribution of asynchronously proliferating RPE-1 cells, and RPE-1 cells treated with the CDK4/6 inhibitor palbociclib for 2 or 4 days. The numbers next to the histograms indicate mean cell size ± standard deviation for the corresponding condition. (E) Cell survival percentage in RPE-1 cells whose cell cycle was synchronized and size was increased through 2 day or 4 day palbociclib-induced cell cycle arrest. After pre-treatment with palbociclib for the indicated number of days, the cells were exposed to 0.4 µM Era2 in the presence of palbociclib for 20 hr. Cell survival percentage was calculated relative to the cells treated only with palbociclib. (F) Cell size distributions of RPE-1 cells that inducibly express shRNA against cyclin D1 gene (CCND1) to slow down the cell cycle and increase cell size. Cells were grown in medium containing 500 ng/mL doxycycline for 4 days to induce shRNA expression and increase cell size. Uninduced cells were grown in the same medium with DMSO in place of doxycycline. (G) Cell survival percentage in control RPE-1 cells and cells expressing an shRNA against cyclin D1 gene (CCND1). Cells were treated with 0.2 µM Era2 for 48 hr, and cell survival percentage was calculated relative to DMSO-treated cells. Cell survival percentages in graphs (B), (E), and (G) are shown as means ± s.e.m.; n=3 biological replicates. A two-tailed unpaired Student’s t-test was used to evaluate the statistical significance of survival percentage differences in panels (E) and (G). (H) Graphical summary: Larger cells are less susceptible to Era2-induced ferroptosis.

Figure 2—source data 1

Original data files for flow cytometry analysis displayed in Figure 2C.

https://cdn.elifesciences.org/articles/111544/elife-111544-fig2-data1-v1.zip

We further confirmed that the effects of cell size on Era2-induced ferroptosis susceptibility are generalizable across cell lines and independent of the cell cycle. To do this, we treated RPE1 cells with palbociclib for 2 or 4 days and then measured their susceptibility to Era2. Both the cells treated with palbociclib for 2 days and for 4 days were arrested in G1, but the cells treated for 4 days were nearly twofold larger than the cells treated for 2 days (Figure 2C and D). This enabled us to isolate the effects of cell size from cell cycle effects and confirm that larger cells were significantly more resistant to Era2-induced ferroptosis compared to their smaller counterparts in the same phase of the cell cycle (Figure 2E).

As an additional means to generate populations of different-sized cells, we used inducible shRNA-mediated cyclin D1 (CCND1) knockdown in RPE-1 cells to generate a population of cells that had larger cell sizes compared to uninduced RPE-1 cells (You et al., 2025). As previously reported, cells that inducibly expressed CCND1 shRNA had a ~30% decrease in Cyclin D1 protein concentration but continued to grow and proliferate, though at a lower division rate (You et al., 2025). As a result, these CCND1 knockdown cells were larger than uninduced cells, and were less susceptible to Era2 (Figure 2F and G).

Our finding that larger cells are less susceptible to ferroptosis induced by Era2 treatment seemingly contradicted findings that large size sensitizes cells to the ferroptosis-inducing compound RSL3 (Chan et al., 2025). Unlike Era2, which acts by inhibiting cystine/glutamate antiporter system xc- and thereby depleting the GSH pool, RSL3 belongs to a different class of ferroptosis inducers, which acts by inhibiting glutathione peroxidase 4 (GPX4) (Yang et al., 2014), a key enzyme that uses GSH to reduce toxic lipid peroxidation. To directly compare the effects of these two classes of compounds in our experimental system, we treated the HT-1080 cells, arrested in G1 for 2–6 days using palbociclib, with RSL3 (Figure 2—figure supplement 1B). Consistent with the published report (Chan et al., 2025) and in contrast to Era2 treatment (Figure 2B), increased cell size during G1 arrest progressively increased sensitivity to RSL3. Such difference in cell responses to the two different classes of ferroptosis inducers has been noted in several reports describing context-dependent responses to different classes of ferroptosis inducers (Chan et al., 2025; Rodencal et al., 2024; Magtanong et al., 2022).

Taken together, our measurements demonstrate that in human cell cultures, larger cells are generally less susceptible to ferroptosis induced by system xc- inhibition (Figure 2H). We observed this result in populations of different-sized cells that were generated through multiple methods, including genetics, FACS sorting, and small molecule-induced cell cycle arrest.

Membrane lipid peroxidation decreases with cell size

Ferroptosis is defined and caused by the accumulation of toxic products from polyunsaturated fatty acid (PUFA) peroxidation in the plasma membrane (Jiang et al., 2021; Dixon and Olzmann, 2024; Figure 3A). We, therefore, next sought to determine how cell size affects ferroptosis-associated lipid peroxidation. Lipid peroxidation can be detected using a ratiometric fluorescent sensor dye BODIPY-C11 581/591 (Dixon et al., 2012; Pap et al., 1999). This dye can be used for ratiometric analysis of lipid peroxidation in flow cytometry, which detects a shift of the fluorescence emission peak from red (∼590 nm) to green (∼510 nm) caused by the oxidation of the polyunsaturated butadienyl portion of this fatty acid analog. When HMEC cells are treated with Era2, which leads to increased lipid peroxidation, we find a decrease in non-oxidized (red) BODIPY-C11 fluorescence and an increase in oxidized (green) BODIPY-C11 as expected (Figure 3B).

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Membrane lipid peroxidation decreases with cell size.

(A) Schematic of pathways regulating ferroptosis. Ferroptosis is induced through the accumulation of toxic peroxidized lipid species in the plasma membrane. The accumulation of peroxidized lipids is prevented by glutathione (GSH) production and GSH-dependent reduction of the peroxidized lipids (Jiang et al., 2021; Dixon and Olzmann, 2024). (B) The ratiometric fluorescent dye BODIPY-C11 581/591 detects lipid peroxidation in live HMEC cells (Dixon et al., 2012; Pap et al., 1999). Oxidation of the polyunsaturated butadienyl portion of this fatty acid analog results in a shift of the fluorescence emission peak from red (∼590 nm) to green (∼510 nm), allowing ratiometric analysis of lipid peroxidation using flow cytometry. Treatment of cells with Era2 increases lipid peroxidation, as indicated by decreased non-oxidized (red) BODIPY-C11 fluorescence and increased oxidized (green) BODIPY-C11 fluorescence. (C) A flow cytometry analysis of lipid peroxidation in different-sized RPE-1 cells within the DMSO-treated cell culture. The side-scatter parameter (SSC-A) was used as a proxy for cell size (Lanz et al., 2022; Tzur et al., 2011). Three biological replicates were performed, and 100,000 events were recorded for each replicate. (D) A quantitative analysis of the flow cytometry data from panel (C): the data were binned into 12 bins by normalized cell size (SSC-A/median SSC-A), and the mean values for oxidized (green) to non-oxidized (red) BODIPY-C11 ratio were plotted for each bin (black line). Gray shaded area denotes the s.e.m. for each bin, and the blue area denotes the standard deviation. (E) For Era2-treated cell populations, live cells were identified and gated in the indicated region using a live/dead cell permeability dye SYTOX Blue. (F) Size-dependent lipid peroxidation in live RPE-1 cells treated with 0.2 µM Era2 for 20 hr. The analysis was performed as in panel (D). (G–I) Proteomics-based analysis of GPX4 (G), ferritin heavy chain (H), and ferritin light chain (I) expression in fluorescence-activated cell sorting (FACS)-sorted small, medium, and large retinal pigment epithelium (RPE-1) cells and primary human lung fibroblasts (HLF). Each line in the plot corresponds to a unique peptide from the indicated proteins identified by mass spectrometry reported in Lanz et al., 2022.

Figure 3—source data 1

Original data files for flow cytometry analysis displayed in Figure 3E,F.

https://cdn.elifesciences.org/articles/111544/elife-111544-fig3-data1-v1.zip

To determine how cell size affects basal lipid peroxidation in cells, we first stained control (DMSO-treated) cells with BODIPY-C11 and subjected them to a flow cytometry analysis using side scatter (SSC-A) as a proxy for cell size (Lanz et al., 2022; Tzur et al., 2011). When we plotted the oxidized to non-oxidized BODIPY-C11 ratio against cell size, we observed a clear anti-correlation between these two parameters (Figure 3C and D). We then performed a similar analysis on Era2-treated cells. For this analysis, we gated only live cells using a fluorescent cell permeability dye SYTOX Blue that stains dead or dying cells to omit cells undergoing Era2-induced death (Figure 3E). This gating allowed us to exclusively focus on the live cells that were not dying. We found that in Era2-treated cell populations, larger cells demonstrated lower levels of lipid peroxidation, similar to untreated cells (Figure 3F). Thus, membrane lipid peroxidation, the terminal cause of ferroptosis, exhibits higher basal levels in smaller cells. This provided a rationale for why smaller cells would require less treatment with Era2 to accumulate enough lipid peroxidation and induce ferroptosis.

Having determined that membrane lipid peroxidation decreases with cell size, we next investigated how the expression of key cellular components regulating this process changes with cell size. The overall lipid oxidation status in the membrane is set largely by the competition between Fe2+-dependent peroxidation of PUFA-containing phospholipids, and glutathione-dependent reduction of peroxidized lipids by GPX4 (Figure 3A). We analyzed size-dependent proteomics data for RPE-1 cells and primary human lung fibroblasts (HLF) and observed no significant size differences in GPX4 expression (Figure 3G). However, ferritin heavy chain (FTH1) and ferritin light chain (FTL) concentrations increased by 1.6-to-2-fold with a two-fold increase in cell size (Figure 3H and I). Ferritin acts as a key iron-storage protein that can promote ferroptosis resistance through iron chelation (Zhang et al., 2022a; Chen et al., 2020a). The observed increase in ferritin concentration with cell size could, therefore, lead to additional Fe2+ ion chelation, which in turn would protect large cells from iron-dependent lipid peroxidation and ferroptosis. However, when we measured the concentration of labile intracellular Fe2+ using a fluorescent probe FerroOrange (Hirayama et al., 2020), we did not observe any size-dependent decrease in labile iron concentration (Figure 3—figure supplement 1A). Previous work also suggested a link between increased sequestration of ferrous iron in lysosomes and resistance to ferroptosis. It was reported that senescent cells, which are also large (Figure 3—figure supplement 2A and B), gain resistance to ferroptosis through lysosomal alkalinization and sequestration of ferrous iron in lysosomes (Loo et al., 2025). We, therefore, tested whether the superscaling of lysosomes observed in large cells (Lanz et al., 2022; You et al., 2025) promotes Era2 resistance through lysosomal iron sequestration. To do this, we stained the cells with the lysosomal iron detection probe Lyso-FerroRed (Saimoto et al., 2025) and measured its scaling using flow cytometry (Figure 3—figure supplement 1B). We observed that the amount of Lyso-FerroRed, and therefore, the amount of lysosomal iron, scaled in direct proportion to cell size, just like the total cellular protein content (Figure 3—figure supplement 1B). These results indicate that iron chelation by ferritin and its sequestration in lysosomes are unlikely to play a crucial role in size-dependent decrease in Era2 sensitivity.

Larger cells have higher concentrations of glutathione

Having shown that lipid peroxidation decreases with cell size and that such a decrease is unlikely to be mediated by changes in labile Fe2+ concentration, we next sought to test if cell size also affects the concentration of GSH, which contributes to oxidized lipid reduction and ferroptosis resistance, and is depleted in cells upon system xc- inhibition (but not GPX4 inhibition) (Jiang et al., 2021; Dixon and Olzmann, 2024; Dixon et al., 2012; Figure 3A). To determine how cell size affects glutathione synthesis, we re-analyzed our previously collected proteomics data, where we compared the proteomes of FACS-sorted small, medium, and large G1-phase RPE-1 cells (Lanz et al., 2022; Figure 3). We found that concentrations of two key proteins involved in GSH biosynthesis, glutamate-cysteine ligase modifier subunit (GCLM) and glutathione synthetase (GSS), increase with cell size. This indicates that larger cells might have higher GSH production rates (Figure 4A).

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Larger cells have higher concentrations of glutathione and enzymes promoting glutathione synthesis.

(A) Proteomics-based analysis indicates the concentrations of key enzymes involved in glutathione production (see Figure 3A) increase with cell size. For this analysis, retinal pigment epithelium (RPE-1) cells were fluorescence-activated cell sorting (FACS)-sorted into populations of small, medium, and large G1 cells, and the proteomes of cells in these bins were analyzed using SILAC mass spectrometry. The primary proteomics data used to plot the concentrations of glutathione synthetase (GSS), glutamate-cysteine ligase catalytic (GCLC), and modifier (GCLM) subunits were taken from our previous work (Lanz et al., 2022). Each line in the plot corresponds to a unique peptide corresponding to the indicated protein that was identified by mass spectrometry. (B) Flow-cytometry-based measurement of cystine/glutamate transporter SLC7A11 (xCT) and cathepsin B (CatB) concentrations in G1-phase RPE-1 cells demonstrates a modest decrease in SLC7A11 and a significant increase in cathepsin B concentrations with cell size. To calculate the concentrations of SLC7A11 and CatB, their amounts were measured with flow cytometry using immunofluorescence and normalized to the amounts of α-Tubulin. The data were binned by cell size, and mean values for each bin were plotted against normalized cell size (solid blue line for SLC7A11 and red line for CatB). Shaded areas denote the s.e.m. for each bin. (C, D) Flow-cytometry-based measurement of glutathione (GSH) amount (C) and total protein amount (D) scaling with cell size in RPE-1 cells. The side scatter parameter (SSC-A) is used as a proxy for cell size (Tzur et al., 2011; Berenson et al., 2019). Three biological replicates were performed, and 100,000 events were recorded in each replicate. (E) GSH concentration in RPE-1 cells plotted against cell size. The GSH concentration is calculated as the ratio of GSH amount to the amount of total protein from data shown in panels (C) and (D). (F–H) Analysis of the flow cytometry data shown in panels (C–E). The data were binned by cell size, and mean values (black lines in the plots) for GSH amount (F), total protein amount (G), and GSH concentration (H) were plotted against normalized cell size (SSC-A/median SSC-A). Gray shaded areas denote the s.e.m. for each bin, and the blue area denotes the standard deviation. The orange line in (F) and (G) is shown for reference and corresponds to a perfect scaling scenario, where the amount of a cell component increases in direct proportion to cell size so that its concentration does not change. The total amount of protein is very close to perfect scaling, while the amount of GSH increases faster than cell size so that its concentration is higher in larger cells. (I, J) Comparison of GSH concentrations in small, medium, and large cells. Based on the flow cytometry data, 5% smallest, 5% largest, and 5% intermediate-sized RPE-1 cells were gated (I), and their GSH concentration (GSH amount per total protein amount) histograms were plotted for each of these size populations (J). The plot shows that the GSH concentration distributions progressively shift towards higher values when cell size increases. The plots in (I–J) are based on the primary data shown in panels (C–D).

Figure 4—source data 1

Original data files for flow cytometry analysis displayed in Figure 4B.

https://cdn.elifesciences.org/articles/111544/elife-111544-fig4-data1-v1.zip

While the upregulation of GSH biosynthesis may promote the resistance of larger cells to ferroptosis, such an upregulation alone cannot explain why larger cells become more resistant to ferroptosis induced by the cystine import inhibitor Era2, but not, for example, by the GPX4 inhibitor RSL3 (Chan et al., 2025; Figure 2B; Figure 2—figure supplement 1B). We found previously that upon mTORC1 inhibition cells can evade cystine deprivation-induced ferroptosis by uptake and catabolism of cysteine-rich extracellular proteins, mostly albumin (Armenta et al., 2022; Figure 3—figure supplement 2C). This process involves albumin degradation in lysosomes, predominantly by cathepsin B (CatB), and subsequent export of cystine from lysosomes to fuel the synthesis of glutathione. Large cells undergo proteome rearrangements similar to those occurring upon mTORC1 inhibition (Zatulovskiy et al., 2022). This suggests that large cells may upregulate CatB expression to bypass the Era2-induced cystine import inhibition via system xc-. To test this hypothesis, we used flow cytometry to measure how the expression of cathepsin B and the system xc- cystine/glutamate transporter SLC7A11 (xCT) - two proteins that supply cells with cystine through two alternative paths - scales with cell size (Figure 4B). We found that SLC7A11 concentration modestly decreases, while CatB concentration significantly increases with cell size (Figure 4B). This shift in the ratio between SLC7A11 and CatB supports the hypothesis that larger cells may rely less on cystine import via system xc- and thus become more resistant to system xc- inhibition by Era2.

To further test the prediction that larger cells have more GSH to protect against ferroptosis, we measured GSH abundance in different-sized cells using flow cytometry. To do this, we stained the cells with the fluorescent GSH probe monochlorobimane (MCB) (Rice et al., 1986; Shrieve et al., 1988). In addition to MCB, we also stained the same cells with the total protein dye CFSE (carboxyfluorescein diacetate succinimidyl ester) (Lanz et al., 2022; Berenson et al., 2019), which allowed us to calculate relative cell size-dependent changes in glutathione concentrations (amounts of glutathione per unit protein mass; Figure 4C–H). While total protein amounts scaled in proportion to cell size (Figure 4G), the amount of MCB-reactive GSH ‘super-scaled’ so that larger cells have higher glutathione concentrations than smaller cells (Figure 4F and H–J). Overall, these findings indicate that larger cells have higher concentrations of glutathione, likely due to their higher concentrations of GSH-producing enzymes and cathepsin B, which could specifically protect them from system xc- inhibition-induced ferroptosis.

Higher concentrations of ACSL4 in smaller cells drives increased membrane lipid peroxidation

Besides the increase in GSH-producing enzyme concentrations with cell size, our proteomics data also indicated that the concentration of another key protein involved in ferroptosis, Acyl-CoA Synthetase Long Chain Family Member 4 (ACSL4), also changed significantly with cell size (Figure 4A). ACSL4 promotes ferroptosis sensitivity by enriching phospholipid and triacylglycerol pools with ω–6 PUFAs that are prone to peroxidation (Doll et al., 2017; Figure 3A). Our proteomics data (Lanz et al., 2022) suggested that the ACSL4 concentration decreases with cell size (Figure 5A). To confirm this finding using flow cytometry, we stained the cells with antibodies against ACSL4. We observed that the ACSL4 concentration indeed decreases with cell size, while the concentration of the control housekeeping protein actin remains constant (Figure 5B). This suggested the lower lipid peroxidation in larger cells may be due in part to lower ACSL4 abundance.

Figure 5 with 1 supplement see all
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Higher expression of ACSL4 in smaller cells drives increased lipid peroxidation and ferroptosis.

(A) Proteomics analysis identified the size-dependent expression of ACSL4, an enzyme that enriches cellular membranes with long polyunsaturated fatty acids prone to peroxidation (Doll et al., 2017). The primary proteomics data were from our previous work (Lanz et al., 2022). Each line in the plot corresponds to a unique peptide from the ACSL4 protein identified by mass spectrometry; the dashed black line indicates the average ACSL4 protein slope. (B) Flow cytometry analysis confirms the decrease of ACSL4 concentration with cell size in HMEC cells. Actin was measured as a reference protein, as its concentration does not change with cell size. To calculate the concentrations of ACSL4 and Actin, their amounts were measured with flow cytometry using immunofluorescence and were normalized to the amounts of α-Tubulin. The data were binned by cell size, and mean values for each bin were plotted against normalized cell size (solid blue line for ACSL4 and black line for Actin). Shaded areas denote the s.e.m. for each bin. (C) Validation of ACSL4 knockout in HT-1080 cells with immunoblotting. Wild-type (WT) HT-1080 cell line and two different ACSL4 knockout clones (KO1 and KO2) were analyzed using antibodies against ACSL4 and α-Tubulin as a loading control. Bar plot shows the quantification of immunoblotting data from three biological replicates. (D) Deletion of ACSL4 eliminates the size-dependence of membrane lipid peroxidation in HT-1080 cells. The plot shows the flow cytometry measurements of lipid peroxidation after 16 hr 1 µM Era2 treatment in wild-type and ACSL4 KO HT-1080 cells (ACSL4 KO1 and ACSL4 KO2 are two different knock-out clones) (Magtanong et al., 2019). The ratio between oxidized (green) BODIPY-C11 and non-oxidized (red) BODIPY-C11 fluorescence is plotted as a metric for lipid peroxidation, and the side-scatter parameter (SSC-A) is used as a proxy for cell size. The flow cytometry data were binned by cell size, and mean values of oxidized to non-oxidized BODIPY-C11 ratios were plotted for each bin (blue solid line corresponds to wild-type cells, orange and red lines correspond to ACSL4 gene-disrupted clones KO1 and KO2). Shaded areas denote the s.e.m. for each bin. (E) Cell size distributions of small, medium, and large G1-arrested ASCL4 KO HMEC cells isolated by FACS sorting. Prior to FACS sorting, the cells were cultured for 24 hr in the presence of 1 µM palbociclib to synchronize cells in G1 phase. Cell size after sorting was measured on a Coulter counter. (F) Cell survival percentage in WT and ACSL4 KO HMEC cells, sorted into small, medium, and large size bins by fluorescence-activated cell sorting (FACS). After sorting, the cells were re-plated in the presence of 1 µM palbociclib to keep them in G1 phase. Cells were then treated with 10 µM Era2 for 52 hr, and the cell survival percentage was calculated relative to palbociclib-only treated cells. Cell survival percentages in graphs are shown as means ± s.e.m. for n=3 biological replicates.

Figure 5—source data 1

TIF file containing original western blots for Figure 5C, indicating the relevant bands and sample names.

https://cdn.elifesciences.org/articles/111544/elife-111544-fig5-data1-v1.zip
Figure 5—source data 2

Original files for western blot analysis displayed in Figure 5C.

https://cdn.elifesciences.org/articles/111544/elife-111544-fig5-data2-v1.zip

To test the role of ACSL4 in size-dependent lipid peroxidation, we measured lipid peroxidation in wild-type and ACSL4 gene-disrupted HT-1080 cells (Magtanong et al., 2019). We stained the cells with BODIPY-C11 and measured fluorescence with flow cytometry. While lipid peroxidation decreased with increasing cell size in wild-type HT-1080 cells, we did not observe any such size-dependence in ACSL4 KO cells (Magtanong et al., 2019; Figure 5C and D). This indicated that ACSL4 activity was required for size-dependent changes in lipid peroxidation, which likely makes smaller cells more susceptible to ferroptosis.

To test the hypothesis that size-dependent expression of ACSL4 mediates the increased resistance of larger cells to ferroptosis, we FACS-sorted ACSL4 KO HMEC cells into small, medium, and large size bins and assessed their responses to Era2 (Figure 5E, F, Figure 5—figure supplement 1). In this experiment, we synchronized cells in G1 phase using palbociclib prior to cell sorting and also incubated the sorted cells in the presence of palbociclib during Era2 treatment to isolate cell size effects from the previously observed confounding effects of the cell cycle on ferroptosis (Figure 2B and E). Consistent with our hypothesis, genetic disruption of ACSL4 reduced the size dependence of the ferroptotic response in Era2-treated HMEC cells (Figure 5F). We note that while genetic disruption of ACSL4 significantly reduced the size dependence of ferroptosis, it did not eliminate the size dependence completely. This might be because ferroptosis is driven by two parallel processes – ACSL4-dependent PUFA incorporation into the membrane and subsequent lipid peroxidation (Figure 3), both of which depend on cell size.

Discussion

In this study, we found that larger cells are less susceptible to ferroptotic cell death induced by the system xc- inhibitor erastin2 (Figure 6). This observation may explain previous findings where genetic alterations that reduce cell size also promote ferroptosis, whereas those that increase cell size inhibit it. For instance, deletion of p21, which decreases cell size, sensitizes to ferroptosis, whereas overexpression of p21, increasing cell size, confers ferroptosis resistance (Tarangelo et al., 2018; Venkatesh et al., 2020). Similarly, deletion of the retinoblastoma protein (RB), which also reduces cell size, promotes ferroptosis. Moreover, further deletion of additional RB family members, p107 and p130, in a triple knockout that drastically reduces cell size, increases sensitivity to ferroptosis (Kuganesan et al., 2021; Sage et al., 2000). Here, we demonstrate that isogenic cell populations differing only in size exhibit different susceptibilities to ferroptosis. This implies that many genetic interactions associated with ferroptosis may actually operate indirectly through their effects on cell size.

Figure 6
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Cell size modulates cell susceptibility to ferroptosis.

Larger cells are less prone to Era2-induced cell death because they generate less peroxidized plasma membrane lipids and more glutathione to reduce those toxic peroxidized lipids.

That increased cell size protects against system xc- inhibitor-induced ferroptosis has important implications for how this form of cell death interacts with the cell division cycle. Cells in G1 phase of the cell cycle were reported to be more susceptible to ferroptosis (Rodencal et al., 2024; Kuganesan et al., 2023), which suggested that ferroptosis inducers could be used in combination with cancer drugs, like the CDK4/6 inhibitor palbociclib, that arrest cells in G1 phase of the cell cycle (Herrera-Abreu et al., 2024). However, while CDK4/6 inhibitors arrest cells in G1, they do not inhibit cell growth, such that the longer they are arrested, the larger the cells grow (Lanz et al., 2022; Crozier et al., 2023; Manohar et al., 2023). This results in a complex, non-monotonic ferroptotic response dynamics in cells treated with CDK4/6 inhibitors (Figure 2B and E). Just following CDK4/6 inhibitor treatment, as more and more cells are arrested in G1 phase, cells become more sensitive to both RSL3- and erastin-induced ferroptosis (Kuganesan et al., 2023; Rodencal et al., 2024). However, the longer the cells are arrested, the larger they become, which further promotes their susceptibility to RSL3 (Figure 2—figure supplement 1B) but reduces their susceptibility to Era2-induced ferroptosis (Figure 2B). The fact that the cell cycle arrest and cell size increase have opposing effects on Era2-induced ferroptosis susceptibility could explain why different studies reported seemingly contradictory results, where sometimes an increased and sometimes a decreased or unchanged sensitivity to system xc- inhibitors was observed depending on the cell type, duration and type of cell cycle arrest (Lee et al., 2024; Kuganesan et al., 2023; Rodencal et al., 2024). Such complex interplay between the cell cycle and cell size effects on ferroptosis suggests that combination therapies utilizing CDK4/6 inhibitors and ferroptosis inducers would have to carefully choose a dosage schedule. Additionally, our data suggest that previously reported resistance of senescent cells to ferroptosis can at least partially be due to the increased cell size, a well-established hallmark of senescence (Davies et al., 2022).

That small cells are more prone to ferroptosis is likely related to their proteome and metabolome composition differing from that of larger cells. Here, we found that larger cells both generate fewer peroxidized membrane lipids and accumulate more glutathione, which can be used to reduce and thus detoxify the peroxidized lipids (Figure 6). Likely, these size-dependent changes in lipid oxidation and glutathione abundance are due to differential cell-size scaling of different metabolic enzymes in the cell. Larger cells also have higher concentrations of iron-chelating protein ferritin and glutathione-producing enzymes GSS and GCLM that protect cells against ferroptosis, and have lower concentrations of the enzyme ACSL4, which promotes ferroptosis. Indeed, the genetic disruption of ACSL4 greatly reduced the size-dependence of membrane lipid peroxidation, a ferroptosis trigger, and makes ferroptosis less size-dependent. Taken together, these observations indicate that the size-dependent remodeling of the proteome underlies much of the size-dependent sensitivity to ferroptosis.

The fact that larger cell size promotes increased ferroptosis resistance to system xc- inhibitors (Era2) but, at the same time, decreased resistance to ferroptosis induced by GPX4 inhibitors (RSL3) suggests that the critical roles in these two types of responses are played by different molecular pathways. This finding supports the notion that the ferroptotic cell death response is highly context-dependent and varies dramatically depending on the cell type and class of stimuli inducing ferroptosis (Rodencal et al., 2024; Magtanong et al., 2022; Soula et al., 2020). While a decreased cell sensitivity to Era2 in larger cells is due to size-dependent increase in glutathione production and decrease in unsaturated lipid incorporation into the membrane by ACSL4, the increased sensitivity to RSL3 is reported to be mediated mainly by ER expansion, iron accumulation, and lipid accumulation and remodeling (Chan et al., 2025). We show that large cells may become resistant specifically to Era2 but not RSL3 through the upregulation of lysosomal function, particularly cathepsin B expression, which enables the uptake and catabolism of cysteine-rich extracellular proteins. A size-dependent shift in the ratio between SLC7A11 and cathepsin B makes large cells less dependent on cystine import via system xc-, and thus, more resistant to Era2. In addition to this, it was reported that RSL3 can induce ferroptosis independently of GPX4 and may target other selenoproteins (DeAngelo et al., 2025; Cheff et al., 2023), which could also contribute to the difference in size-dependent responses to RSL3 and Era2.

In conclusion, our finding that the response of cells to ferroptosis inducers is influenced by cell size points to a broader pattern where cellular decisions are often size-dependent. A general size-dependence of cellular decisions may be driven by widespread changes in protein and metabolite composition taking place as cells grow larger. Indeed, larger cells differ markedly from their smaller counterparts, which likely affects key decisions related to cell death, division, and differentiation. Previous studies, including our own, have explored how cell size influences cell division and contributes to cellular senescence (Lanz et al., 2022; Zatulovskiy et al., 2022; Crozier et al., 2023; Crozier et al., 2022; Manohar et al., 2023; Foy et al., 2023; Wilson et al., 2023; Demidenko and Blagosklonny, 2008). Here, we have demonstrated that cell size also plays a significant role in modulating susceptibility specifically to ferroptotic cell death. Furthermore, research in developmental biology shows that cell size can dictate developmental outcomes, as seen in the asymmetrical division of neurosecretory motor neuron neuroblasts in C. elegans and cell fate determination in Arabidopsis leaves (Sethi et al., 2022; Gong et al., 2023). Thus, we anticipate many cellular decisions will be impacted by cell size, the fundamental mode of cell geometry that sets the basic scale of all intracellular processes.

Materials and methods

Cell culture conditions and cell lines

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Non-transformed hTERT-immortalized human mammary epithelium cells (HMEC-hTERT cells, in this paper referred to simply as HMEC for brevity) were obtained from Stephen Elledge’s laboratory at Harvard Medical School (Solimini et al., 2012) and cultured in MEGM Mammary Epithelial Cell Growth Medium (Lonza CC-3150). HT-1080 cells were purchased from ATCC (Manassas, Virginia, USA). Non-transformed hTERT-immortalized human retinal pigment epithelium cells (cell line RPE-1) were obtained from the Stearns laboratory at Stanford. Both the HT-1080 and RPE-1 cell lines were grown in Dulbecco’s modification of Eagle’s medium (DMEM) with L-glutamine, 4.5 g/L glucose, and sodium pyruvate (Corning), supplemented with 10% FBS (Corning) and 1% penicillin/streptomycin. All cells were cultured at 37 °C with 5% CO2. HT-1080 cell lines expressing nuclear-localized fluorescent protein mKate2 (Nuc::mKate2) were generated by lentiviral transduction of a viral vector at an M.O.I. of 0.3, that directed the expression of nuclear-localized mKate2 (Forcina et al., 2017). Polyclonal mKate2-expressing populations were selected using puromycin (1.5 mg/mL, 72 hr). ACSL4 KO HT-1080 and HMEC cells were generated using a CRISPR/Cas9 system, as described previously (Magtanong et al., 2019). RPE-1 cells carrying a genetic construct for a conditional knockdown of cyclin D1 gene (CCND1) through doxycycline-inducible CCND1 shRNA expression were obtained from the Ioannis Sanidas laboratory at Harvard Medical School. All cell lines tested negative for mycoplasma.

Immunofluorescence cell staining for flow cytometry

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For flow cytometry analysis, cells were grown on dishes to ~50% confluence and harvested by trypsinization. The cells were then fixed with 3% formaldehyde for 10 min at 37 °C and permeabilized with ice-cold 90% methanol for 30 min on ice. Fixed and permeabilized cells were washed once with PBS, blocked with 3% BSA in PBS for 30 min at 37 °C, and then stained with primary antibodies for 2 hr at 37 °C (rat-anti-alpha-Tubulin (Abcam, #ab6160), rabbit-anti-Actin (Sigma-Aldrich, #A2103), rabbit-anti-ACSL4 (Proteintech, #22401–1-AP), rabbit-anti-Cathepsin B (Proteintech, #12216–1-AP), rabbit-anti-xCT/SLC7A11 (Cell Signaling Technology, #12,691T)). The cells were then washed twice with a wash buffer (1% BSA in PBS + 0.05% Tween 20), stained with the fluorophore-conjugated secondary antibodies Alexa Fluor 488 goat anti-rat (Life Technologies, #A11006), Alexa Fluor 594 goat anti-rabbit (Life Technologies, #A11037), and Alexa Fluor 647 goat anti-mouse (Thermo Fisher Scientific, #A32728) at 1:1000 dilution for 1 hr at 37 °C. The cells were then washed again twice. After this treatment, the cells were resuspended in PBS containing 3 µM DAPI for DNA staining, incubated for 10 min at room temperature, and then analyzed on an Attune NxT flow cytometer (Thermo Fisher Scientific). Around 100,000 cells were typically recorded for each sample, and three biological replicates were performed for each experiment. The flow cytometry gating strategy is shown in Figure 4—figure supplement 1. Briefly, single cells were gated based on FSC-A vs SSC-A, then FSC-A vs FSC-H, then SSC-A vs SSC-W plots. From this population of single cells, G1 cells were selected using the Hoechst-A vs FSC-A plot (Figure 4—figure supplement 1).

For plotting, all protein amounts and cell size values were normalized to the means for each sample. To compensate for the nonspecific background staining of cells with antibodies, we measured the fluorescence of cells stained with nonspecific Isotype Control antibodies. We then performed a linear regression of this nonspecific background signal with cell size and subtracted the background fluorescence corresponding to each cell’s size from the actual anti-ACSL4 and anti-actin fluorescence signals measured for each cell. For all binned flow cytometry data plots, the cells below the second and above the 98th cell size percentiles were excluded to remove the extreme outliers. Then, the remaining data were binned by size and plotted as background-corrected average fluorescence intensity for each bin against the bin’s average cell size. Bins with fewer than 200 cells were excluded from the analysis to reduce noise. For protein concentration calculation, the amount of the indicated protein in the cell was normalized to the amount of housekeeping protein alpha-Tubulin, which scales in proportion to cell volume (Lanz et al., 2022).

Lipid peroxidation measurement with BODIPY-C11 581/591 fluorescent dye

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For lipid peroxidation analysis, cells were treated with DMSO or erastin2 (Era2) for 16-20 hr before being harvested by trypsinization. A cell suspension containing ~200,000 cells was then transferred to a 1.5 mL microfuge tube. Cells were then pelleted by centrifugation (400 × g, 5 min) and resuspended in BODIPY-C11 581/591 (5 μM) dissolved in HBSS. A dead cell stain SYTOX Blue (Molecular Probes, #S34857) was added to cell suspensions at a final concentration of 20 nM to identify and exclude from the analysis all non-intact (dead or dying) cells. Cell suspensions were incubated at 37 °C for 20 min and then pelleted (400 × g, 5 min) and resuspended in 0.2 mL of PBS buffer. Samples were strained through a cell strainer prior to flow cytometry analysis. Flow cytometry analysis was performed on an Attune NxT flow cytometer (Thermo Fisher Scientific). Oxidation of BODIPY-C11 581/591 was calculated as the ratio of the green fluorescence (BL1 channel, indicates oxidized probe) to the red fluorescence (YL1 channel, indicates non-oxidized probe) after unstained background fluorescence subtraction, similar to the immunofluorescene measurements described above (Dixon et al., 2012; Pap et al., 1999).

GSH measurements with MCB fluorescent dye

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To measure the GSH amounts in different-sized cells using flow cytometry, we stained the cells with the fluorescent GSH probe MCB (Rice et al., 1986; Shrieve et al., 1988). To do this, the cells were harvested by trypsinization, resuspended in PBS at a density of one million cells per mL, and incubated with 40 μM MCB (Invitrogen, #M1381MP). At the same time we stained cells with MCB, we also stained the same cells with a total protein dye CFSE (CarboxyFluorescein Succinimidyl Ester). For this total protein staining, the CellTrace CFSE dye (Molecular Probes, #C34554) was added to cell suspensions at a 5 μM concentration. The cells were stained with MCB and CFSE for 20 min in a tissue culture incubator (37 °C, 5% CO2) in the dark. The reactions were terminated using 1 mL cold complete medium, followed by centrifugation (400 × g, 5 min, +4 °C). The pelleted cells were then re-suspended in 0.2 mL of PBS, filtered through a 40 µm cell strainer into FACS tubes, and placed on ice. The MCB and CFSE fluorescence signals were measured with an Attune NxT flow cytometer (Thermo Fisher Scientific). GSH concentrations (amounts of glutathione per unit protein mass) in individual cells were calculated as a ratio between the MCB signal (glutathione amount) and CFSE signal (total protein amount).

Intracellular iron detection with fluorescent probes

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To measure labile intracellular iron (Fe2+) concentration, live RPE-1 cells were stained simultaneously with three fluorescent probes - FerroOrange (Dojindo Laboratories, Japan, # F374-10) to measure the amounts of labile Fe2+ in the cell, CellTrace CFSE dye (Molecular Probes, #C34554) to measure total cellular protein amount, and Hoechst 33342 DNA stain (Thermo Scientific, #62249) to determine the cell cycle phase of the cells. Prior to staining, the cells on a dish were washed twice with warm (37 °C) serum-free DMEM and harvested by trypsinization, followed by centrifugation and supernatant removal. The cell pellets were then resuspended in a staining solution containing 1 µM FerroOrange, 5 µM CFSE, and 20 µM Hoechst in warm serum-free DMEM and incubated at 37 °C for 30 min in a 5% CO2 incubator. After the staining the cells were kept on ice and measured on an Attune NxT flow cytometer (Thermo Fisher Scientific). Hoechst signal was used to select G1-phase cells for subsequent analysis.

A similar protocol was used to measure lysosomal Fe2+ amounts, but instead of FerroOrange, the cells were stained with 1 µM Lyso-FerroRed probe (Dojindo Laboratories, Japan, # L270-10). After Lyso-FerroRed staining, the cells were centrifuged and washed twice with serum-free DMEM prior to the flow cytometry measurement.

FACS

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Fluorescence-activated cell sorting was used to sort live cells by their size and cell cycle phase, as described previously (Lanz et al., 2022). Cells were harvested from dishes by trypsinization, stained with 20 µM Hoechst 33342 DNA dye in PBS for 15 min at 37 °C, and then sorted on a BD FACSAria Fusion flow cytometer. Consecutive SSC-A over FSC-A, and FSC-H over FSC-A gates were used to isolate single cells. Then, G1 cells were gated by DNA content (Hoechst staining). Finally, we collected the 5-10% smallest and 5-10% largest cells using the gating based on SSC-A signal as a proxy for cell size. During sorting, all cell samples and collection tubes were kept at 4 °C. To determine the cell size distributions of the collected samples, aliquots were taken from each sorted size bin and measured on a Z2 Coulter counter (Beckman). Sorted cells were replated for subsequent evaluation of their sensitivity to cell-death-inducing chemicals.

Microscopy-based analysis of cell death kinetics

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For the analysis of cell death susceptibility, FACS-sorted small and large G1-phase cells were seeded on a 384-well plate (100–1500 cells per well), allowed to adhere overnight, and then treated with cell-death-inducing compounds for 72 hr. Besides these lethal compounds, the cell culture media also contained 20 nM of the dead-cell dye SYTOX Green (Molecular Probes, #S7020), which permeates dying cells and produces a strong green fluorescent signal in cell nuclei. Cells were imaged on the Essen IncuCyte Zoom and analyzed using scalable time-lapse analysis of cell death kinetics (STACK) as described below. Two or three independent biological replicates were performed for each experimental condition. Each of the biological replicates contained two technical replicates for each condition that were then averaged for cell-death sensitivity quantification. Cell death responses were measured using the scalable time-lapse analysis of cell death kinetics (STACK) technique (Forcina et al., 2017; Inde et al., 2020). Cell lines stably expressing nuclear-localized mKate2 were incubated in medium with 20 nM SYTOX Green (SG) and the indicated cell-death-inducing compounds. Counts of live (mKate2+) and dead (SG+) objects were obtained from images acquired every 4 hr on the Essen IncuCyte Zoom (Essen BioScience, Ann Arbor, MI). The following image extraction parameter values were used for automated image analysis. For SG +objects (dead cells): Adaptive Threshold Adjustment 3; Edge Split On; Edge Sensitivity −7; Filter Area min 0 μm2, max 750 μm2. For mKate2+objects (live cells): Adaptive Threshold Adjustment 2.5; Edge Split On; Edge Sensitivity −2; Filter Area min 50 μm2, maximum 8100 μm2; Eccentricity max 0.9. For Overlap objects: Filter area min 50 μm2, maximum 8100 μm2. Counts were exported to MS Excel (Microsoft Corporation, Redmond, WA) and lethal fraction (LF) scores were computed from mKate2+ and SG+ counts, with the additional step of removing ‘overlap’ double positive counts from live cell counts at each timepoint, as described before (Forcina et al., 2017; Inde et al., 2020). LF scores were exported to Prism 9.0.1 (GraphPad Software, La Jolla, CA) for curve fitting and data plotting.

Immunoblotting

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For immunoblotting, cells were lysed in RIPA lysis buffer on ice. Proteins from lysates were separated on 10% SDS-PAGE gels and transferred to nitrocellulose membranes. Membranes were then blocked with SuperBlock (TBS) Blocking Buffer (Thermo Fisher Scientific) and incubated overnight at 4 °C with primary antibodies in 3% BSA solution in PBS. The primary antibodies used were: anti-ACSL4 (Sigma-Aldrich, #SAB2701949; 1:2500 dilution) and anti-tubulin (Millipore Sigma, #MS581P1; 1:2000 dilution). The primary antibodies were detected using the fluorescently labeled secondary antibodies IRDye 680LT Goat anti-Mouse IgG (LI-COR 926–68020) and IRDye 800CW Goat anti-Rabbit IgG (LI-COR 926–32211). Membranes were imaged on a LI-COR Odyssey CLx and analyzed with LI-COR Image Studio software.

Data quantification, statistical analysis, and software

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Lethal fraction calculations and statistical analyses of cell death were performed using Microsoft Excel 14.6.0 (Microsoft Corporation, USA). Flow cytometry data were analyzed using FlowJo 10.6.1 (FlowJo LLC, USA) and MATLAB R2022b (MathWorks, USA). Graphing was performed using Prism 9 (GraphPad Software, La Jolla, CA).

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting files.

References

    1. Bell CD
    2. Waizbard E
    (1986)
    Variability of cell size in primary and metastatic human breast carcinoma
    Invasion & Metastasis 6:11–20.
    1. Rice GC
    2. Bump EA
    3. Shrieve DC
    4. Lee WL
    5. Kovacs MK
    (1986)
    Quantitative analysis of cellular glutathione by flow cytometry utilizing monochlorobimane: some applications to radiation and drug resistance in vitro and in vivo
    Cancer Research 46:6105–6110.
    1. Shrieve DC
    2. Bump EA
    3. Rice GC
    (1988)
    Heterogeneity of cellular glutathione among cells derived from a murine fibrosarcoma or a human renal cell carcinoma detected by flow cytometric analysis
    The Journal of Biological Chemistry 263:14107–14114.

Article and author information

Author details

  1. Evgeny Zatulovskiy

    1. Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
    2. Department of Biology, Stanford University, Stanford, United States
    Contribution
    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    ez225@cam.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7847-5829

  2. Magdalena B Murray

    Department of Biology, Stanford University, Stanford, United States
    Contribution
    Formal analysis, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6306-4568

  3. Shuyuan Zhang

    Department of Biology, Stanford University, Stanford, United States
    Contribution
    Validation, Investigation, Visualization, Writing – review and editing
    Competing interests
    No competing interests declared

  4. Scott J Dixon

    Department of Biology, Stanford University, Stanford, United States
    Contribution
    Resources, Supervision, Funding acquisition, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6230-8199

  5. Jan M Skotheim

    1. Department of Biology, Stanford University, Stanford, United States
    2. Chan Zuckerberg Biohub San Francisco, San Francisco, United States
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Writing – original draft, Writing – review and editing
    For correspondence
    skotheim@stanford.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8420-6820

Funding

Center for Cancer Research (P01 CA254867)

  • Jan M Skotheim

National Institute of General Medical Sciences (R01 GM122923)

  • Scott J Dixon

Medical Research Council (MR/X020290/1)

  • Evgeny Zatulovskiy

Chan Zuckerberg Initiative (Biohub Investigator Award)

  • Jan M Skotheim

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Leslie Magtanong for assistance with the initial cell death screen design and all members of the Zatulovskiy and Skotheim laboratories for valuable discussions and feedback on this project. Cell sorting for this project was performed on an instrument in the Stanford Shared FACS Facility purchased by Parker Institute for Cancer Immunotherapy, and an instrument in the University of Cambridge, Department of Pathology FACS facility. We thank both FACS facilities for their assistance. This work was supported by a Chan Zuckerberg Biohub Investigator Award (JMS), the NIH (P01 CA254867 grant to JMS and R01 GM122923 to SJD), and the Medical Research Council (MR/X020290/1 Career Development Award to EZ).

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© 2026, Zatulovskiy et al.

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  1. Evgeny Zatulovskiy
  2. Magdalena B Murray
  3. Shuyuan Zhang
  4. Scott J Dixon
  5. Jan M Skotheim
(2026)
Cell size modulates ferroptosis susceptibility
eLife 15:RP111544.
https://doi.org/10.7554/eLife.111544.2

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