Immune-intrinsic MLKL regulates dendritic cell maturation and CD8⁺ T cell effector function in anti-tumor immunity

Abstract

Background

Anti-tumor immunity relies on coordinated interactions between dendritic cells (DCs) and cytotoxic CD8⁺ T cells to eliminate malignant cells. Mixed lineage kinase domain-like protein (MLKL), a key executioner of necroptosis, has recently been implicated in cancer through necroptosis-independent functions and is being explored as a therapeutic target. However, most studies have focused on tumor cells, whereas the immune-intrinsic functions of MLKL remain poorly understood. In particular, MLKL expression across immune cell subsets and its role in anti-tumor immunity have not been systematically investigated.

Results

Transcriptomic analyses revealed broad MLKL expression across multiple immune cell subsets, including neutrophils, monocytes, dendritic cells, and activated/memory Tcell subsets. Consistent with these findings, MLKL was upregulated in primary mouse immune cells following activation, including Tcell stimulation, bone marrow-derived dendritic cell (BMDC) maturation, and M1 macrophages. Pharmacological inhibition of MLKL impaired BMDC maturation, reduced DC-mediated CD8⁺ T cell priming, and suppressed CD8⁺ T cell activation, proliferation, and cytotoxic killing of B16-OVA tumor cells. Integrated single-cell RNA sequencing analyses from cancer patients receiving immune checkpoint blockade (ICB) further showed that MLKL expression was enriched in activated and effector CD8⁺ T cell subsets. Donor-level analyses showed higher MLKL expression in T cells from ICB responders than in those from non-responders, particularly in post-treatment samples. These findings associate MLKL expression with activated and effector anti-tumor Tcell states and suggest its potential relevance to ICB response.

Conclusions

Collectively, these findings show that MLKL is upregulated during immune activation and contributes to DC maturation and CD8⁺ T cell effector responses. Our findings highlight the context-dependent roles of MLKL across tumor and immune compartments and emphasize the importance of considering immune-intrinsic MLKL functions in the development of MLKL-targeted therapies for precision cancer immunotherapy.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s13062-026-00931-z.

Background

Anti-tumor immunity refers to the capacity of the innate and adaptive immune system to recognize and eliminate malignant cells [1, 2]. Effective anti-tumor immunity relies on coordinated interactions between dendritic cells (DCs) and cytotoxic CD8⁺ T cells. DCs initiate this process by presenting tumor antigen, providing co-stimulatory signals, and supporting the priming and expansion of tumor-specific CD8⁺ T cells which subsequently eliminate tumor cells [1, 3]. In recent years, immune checkpoint blockade (ICB) targeting inhibitory receptors have emerged as one of the most effective cancer immunotherapies by restoring anti-tumor Tcell responses [4]. However, response rates remain variable, and only a subset of patients achieving clinical benefit. Increasing evidence indicates that the presence of tumor-infiltrating CD8⁺ T cells is associated with responsiveness to ICB therapies [5, 6]. Nevertheless, the presence of CD8⁺ T cells alone is insufficient to generate effective anti-tumor immunity [4, 7, 8]. DCs serve as key partners of T cells and are indispensable for the initiation of adaptive immune responses [1, 3, 7–9]. Therefore, understanding the molecular mechanisms that regulate DC activation and CD8⁺ T cell function is essential for improving anti-tumor immunity and cancer immunotherapy.

Mixed lineage kinase domain-like protein (MLKL) is the executioner and downstream target of receptor-interacting protein kinase 3 (RIPK3) in necroptosis, a regulated form of inflammatory cell death [10–12]. Unlike apoptosis, necroptosis induces the release of damage-associated molecular patterns (DAMPs) and inflammatory mediators that can promote immune activation and inflammation, implicating in host defense against infection, inflammatory diseases, and cancers [13–16]. However, accumulating evidence suggests that MLKL has functions beyond necroptosis, including endosomal trafficking [17, 18], extracellular vesicle formation [17], NLRP3 inflammasome activation [19, 20], and membrane protein trafficking [21, 22]. MLKL has been reported to regulate the recycling of immune regulatory molecules such as PD-L1 through endosomal trafficking pathways involving the retromer complex [21]. In addition, MLKL has been implicated in regulating receptor trafficking, as its depletion prolongs TNFR1 retention within early endosomes and delays its degradation in response to 5-fluorouracil [22]. Collectively, these findings suggest that MLKL may contribute to the regulation of membrane protein trafficking, which could modulate immune-related processes. Given these diverse functions, MLKL has emerged as a potential therapeutic target. Recent studies have explored strategies to target MLKL, including the development of MLKL degraders [23]. While most studies have focused on the role of MLKL in tumor cells, tumors are surrounded by complex tumor microenvironment (TME) that includes immune cells capable of shaping tumor progression and responses to immunotherapy [24, 25]. Therefore, understanding the immune-intrinsic role of MLKL within immune cells is important when considering MLKL as a therapeutic target, as its inhibition may adversely affect anti-tumor immune responses.

Recent studies have suggested that MLKL may regulate immune responses in DCs. For example, MLKL has been reported to promote DC maturation [26] and modulate Tcell proliferation under certain conditions [27], indicating a potential role of MLKL in regulating immune cell activation. However, the expression profile of MLKL across major immune cell subsets during immune activation has not been systematically characterized. Moreover, its functional roles in DC maturation and CD8⁺ T cell activation, proliferation, and effector function in anti-tumor immunity remain largely unexplored.

To address this gap, we first explored the expression pattern of MLKL across immune cell subsets using publicly available transcriptomic datasets and then investigated its functional role in anti-tumor immunity using primary mouse immune cells. Furthermore, to extend our findings toward clinical relevance, we analyzed integrated single-cell RNA sequencing datasets from cancer patients receiving ICB. Our findings provide new insights into the immune-intrinsic role of MLKL and highlight the importance of considering its function in immune cells when developing MLKL-targeted therapies, as MLKL inhibition in tumor cells may influence anti-tumor immune responses.

Methods

Analysis of MLKL mRNA expression across immune cell subsets in publicly transcriptomic datasets

MLKL mRNA expression across immune cell subsets was analyzed using gene expression data obtained from the Human Protein Atlas (HPA) Monaco dataset, which summarizes gene expression levels across 30 human immune cell types [28]. MLKL expression levels were visualized using boxplots, with the Y-axis representing normalized transcripts per million (nTPM) and the X-axis representing different immune cell subsets. In addition, MLKL expression in tumor-infiltrating immune cells within tumor microenvironment (TME) was examined using single-cell RNA sequencing (scRNA-seq) datasets from non-small cell lung cancer (NSCLC) cohorts (GSE151537 and GSE117570) and a melanoma cohort (GSE120575). These datasets were analyzed using the Tumor Immune Single-cell Hub 2 (TISCH2) bioinformatic tool, a scRNA-seq database focused on the TME that provides detailed cell-type annotation at the single-cell level, enabling exploration of immune cell composition across different cancer types [29].

Isolation of mouse immune cells

Bone marrow-derived dendritic cells (BMDCs) and bone marrow-derived macrophages (BMDMs) were generated from 8-week-old female C57BL/6J mice (Taconic Biosciences, Rensselaer, New York, USA). All animal procedures were approved by the Institutional Animal Care and Use Committee (IACUC; 2022N000118) and performed in accordance with institutional guidelines. Following euthanasia, bone marrow (BM) cells were flushed from femurs and tibias using RPMI 1640 medium supplemented with 2% fetal bovine serum (FBS). Red blood cells (RBCs) were lysed using ammonium-chloride-potassium (ACK) lysis buffer for 4 min at room temperature and neutralized with RPMI 1640 containing 2% FBS. Cells were then centrifuged and resuspended in complete RPMI 1640 medium.

For BMDC differentiation, BM cells were cultured in complete RPMI 1640 containing 20 ng/mL GM-CSF (R&D systems, Minneapolis, Minnesota, USA) for 6 days. Fresh medium containing GM-CSF was added on day 3. On day 6, suspension and loosely adherent cells were collected as immature dendritic cells (imDCs).

For BMDM differentiation, BM cells were cultured in complete RPMI 1640 containing 25 ng/mL M-CSF (R&D Systems) for 7 days. Adherent cells were harvested on day 7 as unpolarized macrophages (M0).

For Tcell isolation, spleens and lymph nodes (LNs) were harvested from 8-week-old C57BL/6J mice and mechanically dissociated in RPMI 1640 containing 2% FBS and passed through a 70 μm nylon cell strainer to obtain single-cell suspensions. RBCs were lysed using ACK buffer. CD4⁺ and CD8⁺ T cells were isolated by negative selection using magnetic bead-based isolation kits (Miltenyi Biotec, Bergisch Gladbach, Germany) according to the manufacturer’s instructions. All cells were maintained at 37 °C in a humidified incubator with 5% CO₂.

BMDC maturation

On day 6 of differentiation, imDCs were seeded in 96-well U-bottom plates and stimulated with 1 µg/mL lipopolysaccharide (LPS, PeproTech, Cranbury, New Jersey, USA) for 24 h. To determine the role of MLKL in BMDC maturation, BMDCs were pretreated with MLKL inhibitor (GW806742X; Sigma-Aldrich, St. Louis, Missouri, USA) for 1 h prior to LPS stimulation. Following stimulation, cells were harvested and stained for surface expression of MHC-II, PD-L1 and the co-stimulatory molecule CD86. BMDCs were identified as CD11c⁺ cells by Cytek Aurora™ flow cytometry (Cytek Biosciences, Fremont, California, USA) and analyzed using FlowJo software (BD Biosciences, Franklin Lakes, New Jersey, USA). Gating strategy identifying BMDCs is shown in Supplementary Fig. 1.

T cell stimulation

Purified naïve CD4⁺ and CD8⁺ T cells were seeded in 96-well U-bottom plates pre-coated with anti-CD3 and anti-CD28 (BioXcell, Lebanon, New Hampshire, USA) to mimic Tcell receptor (TCR) stimulation and incubated for 48 h at 37 °C in complete RPMI 1640 medium. To determine the role of MLKL in CD8⁺ T cell activation, CD8⁺ T cells were pretreated with MLKL inhibitor (GW806742X) for 1 h prior to TCR stimulation. Following stimulation, cells were harvested and stained for activation and effector markers including CD44, PD-1, IFN-γ, perforin, and TNF-α. Data were acquired by Cytek Aurora™ flow cytometry and analyzed using FlowJo software. Gating strategy identifying CD4⁺ and CD8⁺ T cells is shown in Supplementary Fig. 1.

In vitro CD8⁺T cell proliferation

Purified naïve CD8⁺ T cells were stained with CellTrace CFSE Cell Proliferation dye (Thermo Fisher Scientific, Waltham, Massachusetts, USA) according to the manufacturer’s instructions and subsequently seeded in 96-well U-bottom plates pre-coated with anti-CD3 and anti-CD28. Cells were then cultured for 48 h at 37 °C in complete RPMI 1640 medium. To determine the role of MLKL in CD8⁺ T cell proliferation, CD8⁺ T cells were pretreated with MLKL inhibitor (GW806742X) for 1 h prior to TCR stimulation. Following stimulation, CFSE dilution was analyzed by Cytek Aurora™ flow cytometry to evaluate cell proliferation. Data were analyzed using FlowJo software.

M1/M2 macrophage polarization

After 7 days of differentiation, BM cells were differentiated into unpolarized M0 macrophages. M0 macrophages were then polarized into M1 macrophages using 100 ng/mL LPS and 20 ng/ml IFN-γ (PeproTech) for 24 h, or into M2 macrophages by stimulating with 20 ng/mL IL-4 (PeproTech). To confirm successful polarization, cells were detached using 0.05% Trypsin (Corning, New York, USA) and stained for surface expression of CD86 as an M1 marker and CD206 as an M2 marker. BMDMs were identified as F4/80⁺ cells by Cytek Aurora™ flow cytometry. Data were analyzed using FlowJo software. Gating strategy identifying BMDMs is shown in Supplementary Fig. 1.

In vitro DC-Tcells priming

Immature dendritic cells (imDCs) isolated from C57BL/6J mice were seeded in 96-well U-bottom plates and incubated with or without 10 µg/mL ovalbumin (OVA) (InvivoGen, San Diego, California, USA) in the presence or absence of MLKL inhibitor (GW806742X) for 24 h. After incubation, BMDCs were washed with fresh medium to remove residual OVA and MLKL inhibitor before subsequent co-culture. Naïve CD8⁺ T cells were isolated from the spleen and lymph nodes of OT-I mice (Taconic Biosciences) by negative selection. CD8⁺ T cells were then stained with CellTrace CFSE Cell Proliferation dye and co-cultured with antigen-loaded BMDCs at the ratio of 5:1 for 72 h. CD8⁺ T cell proliferation was assessed by CFSE dilution using Cytek Aurora™ flow cytometry and analyzed with FlowJo software.

Tumor killing assay

To assess CD8⁺ T cell-mediated anti-tumor cytotoxicity, naïve CD8⁺ T cells were isolated from the spleen and lymph nodes of OT-I mice. Cells were pretreated in the presence or absence of MLKL inhibitor (GW806742X) for 1 h prior to TCR stimulation. Activated CD8⁺ T cells were then co-cultured with B16-OVA melanoma cells [30] at effector-to-target (E:T) ratios of 8:1, 4:1, 2:1, and 1:1 for 72 h. Tumor cell viability was assessed by flow cytometry. Live and dead cells were discriminated using LIVE/DEAD™ Fixable Near-IR Dead Cell Stain Kit (Thermo Fisher Scientific) and CD8 staining was used to distinguish CD8⁺ T cells from tumor cells. Cytotoxicity was determined based on the absolute number of viable tumor cells. Gating strategy is shown in Supplementary Fig. 2.

RNA preparation and reverse transcription quantitative PCR (RT-qPCR)

Total RNA was isolated from immune cells using TRIzol™ reagent (Thermo Fisher Scientific) according to the manufacturer’s protocol. RNA concentration and purity were determined using a Nanodrop spectrophotometer. A total of 500 ng RNA was reverse-transcribed into cDNA using the PrimeScript™ RT reagent Kit (Takara Bio Inc., Shiga, Japan) following the manufacturer’s protocol. Reverse transcription quantitative PCR (RT-qPCR) was performed using LightCycler^® 480 SYBR Green I Master Mix (Roche, Mannheim, Germany). The mRNA expression levels of Mlkl and Ripk3 were analyzed and normalized to β-actin as an internal control. Relative gene expression was calculated using the 2^–ΔΔCt method. Pre-validated primer sequences obtained from OriGene (Rockville, Maryland, USA) were as follows: Mlkl, 5’-CTGAGGGAACTGCTGGATAGAG-3’ (Forward) and 5’-CGAGGAAACTGGAGCTGCTGAT-3’ (Reverse); Ripk3, 5’-GAAGACACGGCACTCCTTGGTA-3’ (Forward) and 5’-CTTGAGGCAGTAGTTCTTGGTGG-3’ (Reverse); Actb, 5’- CATTGCTGACAGGATGCAGAAGG-3’ (Forward) and 5’- TGCTGGAAGGTGGA CAGTGAGG-3’ (Reverse).

Western blot analysis

Cells were washed with phosphate-buffered saline (PBS; Invitrogen, Waltham, Massachusetts, USA) and lysed in RIPA buffer (Thermo Fisher Scientific) containing protease inhibitors (Thermo Fisher Scientific). Lysates were clarified by centrifugation at 12,000 rpm for 10 min, and the supernatants were collected. Protein concentrations were determined using a BCA assay (Thermo Fisher Scientific). Proteins were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE; Bio-Rad, Hercules, California, USA) and transferred onto polyvinylidene difluoride (PVDF) membranes (Merck Millipore, Darmstadt, Germany). Membranes were blocked using EveryBlot Blocking Buffer (Bio-Rad) and incubated overnight at 4 °C with the following primary antibodies: anti-MLKL (ab184718; Abcam, Cambridge, UK), anti-RIPK3 (#95702; Cell Signaling Technology, Danvers, Massachusetts, USA), anti-pMLKL (ab196436; Abcam) and anti-β-Actin (#4967; Cell Signaling Technology) as a loading control. Membranes were then incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies (Cell Signaling Technology) for 1 h at room temperature. Protein bands were detected using SuperSignal™ West Pico PLUS Chemiluminescent Substrate (Thermo Fisher Scientific) and visualized with iBright Imaging System (Thermo Fisher Scientific).

Flow cytometry

Cells were washed with MACS buffer (DPBS containing 1% FBS and 2 mM EDTA) and incubated with anti-mouse CD16/CD32 (Fc Block; BD Biosciences) for 10 min at 4 °C to block Fc receptors. Cell viability was assessed using the LIVE/DEAD™ Fixable Near-IR Dead Cell Stain Kit according to the manufacturer’s instructions. For surface staining, cells were incubated with fluorochrome-conjugated antibodies in MACS buffer for 20 min at 4 °C followed by washing and resuspension in MACS buffer for data acquisition. For intracellular cytokine staining, cells were stimulated with 50 ng/mL Phorbol 12-myristate 13-acetate (PMA) and 500 ng/mL ionomycin for 4–6 h in the presence of 1X GolgiPlug protein transport and GolgiStop protein transport inhibitors (BD Biosciences). Cells were then fixed and permeabilized using a Fixation/Permeabilization kit (Invitrogen) and stained with intracellular antibodies in permeabilization buffer according to the manufacturer’s protocol.

The following antibodies were used for staining: PE/Dazzle™ 594 anti-mouse CD4 (100456; BioLegend, Koblenz, Germany), BUV395 anti-mouse CD8 (563786; BD Biosciences), BV605 anti-mouse PD-1 (CD279) (135220; BioLegend), BV785 anti-mouse CD44 (103059; BioLegend), Pacific blue anti-mouse Perforin (154312; BioLegend), APC-IFN-γ (505810; BioLegend), Alexa Fluor 700 anti-mouse TNF-α (506338; BioLegend), PE/Cyanine7 anti-mouse CD11c (117318; BioLegend), BV605 anti-mouse CD86 (105037; BioLegend), Pacific blue anti-mouse MHC-II (I-A/I-E) (107620; BioLegend), BV650 anti-mouse PD-L1 (B7-H1)(CD274) (124336; BioLegend), FITC anti-mouse F4/80 (123107; BioLegend), Spark Red™ 718 anti-mouse CD206 (285215; BioLegend). Data were acquired using a Cytek Aurora™ spectral flow cytometer and analyzed using FlowJo software.

Analysis of MLKL expression across immune cell subsets in publicly available scRNA-seq datasets from patients treated with ICB.

To investigate MLKL expression in the TME, we queried a publicly available web-based single-cell resource generated by Gondal et al. [31], which integrates eight curated, quality-checked, and preprocessed scRNA-seq datasets from nine cancer types treated with ICB (Supplementary Table 1). The resource includes standardized cell annotations and outcome metadata across 223 patients, enabling comparative analyses across immune cell populations and between responder and non-responder groups. We further downloaded the processed cell-type pseudobulk expression matrices with standardized cell annotations, treatment timepoint labels, and clinical outcome metadata. Raw cell-type labels were harmonized into broad immune compartments, including T cells, B cells, myeloid/macrophage cells, DC/pDC, and NK cells. Expression analyses were performed at the donor level using normalized pseudobulk expression values. When multiple rows were available for the same donor within a compartment or timepoint, values were averaged to generate one donor-level profile for each comparison. Fine Tcell state analyses were restricted to the Yost et al. basal cell carcinoma cohort [32], which provided detailed Tcell state annotations. RIPK3 and RIPK1 were analyzed in parallel with MLKL as necroptosis-pathway comparator genes.

Statistical analysis

All experiments were performed in three independent biological replicates. Data are presented as mean ± standard deviation (S.D.). Statistical comparisons between two groups were performed using Student’s t-test. For comparisons involving more than two groups, Kruskal-Wallis test was used. Statistically significant was considered when P-value is less than 0.05. All statistical analyses were performed using GraphPad Prism 11 software. For the publicly available scRNA-seq ICB cohort analysis, statistical testing was performed separately at the donor level using normalized cell-type pseudobulk expression values. Responder-versus-non-responder comparisons were performed using two-sided Mann-Whitney U tests. Paired pre- versus post-treatment comparisons were performed using two-sided Wilcoxon signed-rank tests. Comparisons across multiple immune compartments or Tcell states were performed using Kruskal-Wallis tests. For donor-level pseudobulk differential expression analysis, gene-wise two-sided Mann-Whitney U tests were performed with Benjamini-Hochberg correction across tested genes. Adjusted q values are reported where multiple-testing correction was applied.

Results

MLKL is broadly expressed across immune cell populations in publicly available transcriptomic datasets

To investigate the expression profile of MLKL in immune cells, we first analyzed publicly available transcriptomic datasets. Specifically, the Human Protein Atlas (HPA) Monaco dataset, which summarizes transcript profiles across 30 human immune cell types, was utilized. As shown in Fig. 1A, MLKL was broadly expressed across multiple immune cell populations, with the highest expression was observed in neutrophils and monocytes, while relatively elevated expression was also detected in dendritic cells (DCs) and several activated/memory Tcell subsets. To further examine the distribution of MLKL within the tumor microenvironment (TME), we analyzed publicly available single-cell RNA sequencing (scRNA-seq) datasets from two non-small cell lung cancer (NSCLC) cohorts (GSE151537 and GSE117570) and one melanoma cohort (GSE120575) using the TISCH2 platform. UMAP visualizations of the annotated cell populations are shown in Fig. 1B-C and Supplementary Fig. 3. Consistent with the HPA dataset, MLKL expression was broadly detected across multiple tumor-infiltrating immune cell subsets, including CD8⁺ T cells, DCs, macrophages, and proliferating Tcell populations. Collectively, these analyses demonstrate broad MLKL expression across diverse immune cell populations. However, further experimental studies are required to determine the functional significance of MLKL expression in these immune cell populations.

Fig. 1.

MLKL expression across immune cell subsets in publicly transcriptomic datasets. (A) MLKL mRNA expression across 30 human immune cell types obtained from the Human Protein Atlas (HPA) Monaco dataset. Gene expression levels are presented as normalized transcripts per million (nTPM), an RNA sequencing-based normalization metric for transcript abundance, where higher nTPM values indicate higher gene expression. (B-C) UMAP visualization of annotated cell populations, MLKL feature plots, and heatmap analyses showing relative MLKL expression levels [log(TPM/10 + 1)] across annotated cell types in tumor-infiltrating immune cells from single-cell RNA sequencing datasets (B) GSE117570, and (C) GSE120575, analyzed using the TISCH2 bioinformatic tool

Generation and validation of immune activation and polarization models

To profile MLKL and RIPK3 expression across major immune cell subsets, primary immune cells were isolated from C57BL/6J mice. Naïve CD4⁺ and CD8⁺ T cells were isolated from the spleen and lymph nodes (LNs), whereas bone marrow (BM) cells were cultured with GM-CSF or M-CSF to generate bone marrow-derived dendritic cells (BMDCs) and bone marrow-derived macrophages (BMDMs), respectively. To establish models for investigating changes in MLKL and RIPK3 expression during immune activation and polarization, naïve CD4⁺ and CD8⁺ T cells were stimulated with anti-CD3 and anti-CD28 antibodies to mimic Tcell receptor (TCR) activation. In parallel, BMDCs were matured with LPS, and macrophages were polarized toward M1 or M2 phenotypes using IFN-γ plus LPS or IL-4, respectively (Supplementary Fig. 5A). As expected, stimulation of CD8⁺ T cells with anti-CD3 and anti-CD28 significantly increased the expression of activation markers CD44 and PD-1 compared with unstimulated controls (Supplementary Fig. 5B). Similarly, activation of CD4⁺ T cells led to increased PD-1 expression and elevated intracellular TNF-α production (Supplementary Fig. 5C), confirming successful Tcell activation. For BMDCs, BMDCs stimulated with LPS significantly increased expression of the maturation markers CD86 and MHC-II, confirming successful BMDC maturation (Supplementary Fig. 5D). For macrophages, M0 macrophages stimulated with IFN-γ plus LPS showed significantly increased CD86 expression, a marker of M1 polarization, whereas those treated with IL-4 exhibited elevated CD206 expression, a marker of M2 polarization (Supplementary Fig. 5E). These results confirm successful polarization into M1 and M2 macrophage phenotypes, respectively.

MLKL is upregulated upon immune activation in Tcells, BMDCs, and M1 macrophages at both mRNA and protein levels

Following validation of the immune activation and polarization models, we examined the expression of the necroptosis-related molecules, MLKL and RIPK3, using RT-qPCR. As shown in Fig. 2A-B, Mlkl mRNA expression was significantly upregulated following TCR stimulation in both CD8⁺ and CD4⁺ T cells, with peak expression observed at 48 h. In BMDCs, LPS stimulation induced Mlkl mRNA expression, with maximal induction observed at 24 h (Fig. 2C). In macrophages, Mlkl mRNA expression was also elevated under inflammatory conditions, with significantly higher levels in M1 macrophages than in M0 and M2 macrophages (Fig. 2D). Ripk3 mRNA expression was likewise increased following immune activation across all immune cell subsets examined. Notably, Mlkl exhibited a consistently stronger induction across the immune activation models (Fig. 2E-H). Consistent with the mRNA results, Western blot analysis demonstrated increased MLKL and RIPK3 protein expression in activated CD8⁺ and CD4⁺ T cells, BMDCs, and M1 macrophages (Fig. 2I-L and Supplementary Fig. 6A). To determine whether immune activation was accompanied by canonical MLKL activation, we next evaluated phosphorylated MLKL (pMLKL) by Western blot analysis. Robust pMLKL was readily detected in TNF-α/SMAC mimetic/z-VAD-FMK (TSZ)-treated M1 BMDMs, which served as a positive control for canonical necroptosis. In contrast, pMLKL was undetectable in activated CD8⁺ T cells, BMDCs, or polarized macrophages under the experimental conditions examined. Weak pMLKL expression was observed in activated CD4⁺ T cells, although the signal was substantially lower than that detected in the TSZ-treated positive control (Supplementary Fig. 6B-D).

Fig. 2.

MLKL expression is upregulated upon immune activation at both mRNA and protein levels. Relative Mlkl mRNA expression in (A) CD8⁺ T cells (B) CD4⁺ T cells (C) BMDCs and (D) polarized macrophages following stimulation, as determined by RT-qPCR. Expression levels were normalized to β-actin and are presented relative to unstimulated controls. Relative Ripk3 mRNA expression in (E) CD8⁺ T cells (F) CD4⁺ T cells (G) BMDCs and (H) polarized macrophages following stimulation, as determined by RT-qPCR. Representative Western blot showing MLKL protein expression in (I) CD8⁺ T cells (J) CD4⁺ T cells (K) BMDCs and (L) polarized macrophages following stimulation. β-actin was used as a loading control

Together, these findings demonstrate that MLKL is robustly induced during immune activation across multiple immune cell subsets, whereas canonical MLKL phosphorylation is not readily detectable under the experimental conditions examined. These observations provided the basis for subsequent functional investigation of MLKL in immune cells.

MLKL inhibition impairs BMDC maturation and DC-mediated Tcell priming

Given the increased MLKL expression observed during immune activation, we next investigated whether MLKL contributes to immune cell activation in anti-tumor immunity. Therefore, we examined the functional roles of MLKL in BMDCs and CD8⁺ T cells, two key components of anti-tumor immunity. As MLKL inhibition is being explored as a potential cancer therapeutic strategy, we utilized a commercially available pharmacological inhibitor (GW806742X) of MLKL to investigate its effects on immune function. To examine whether MLKL contributes to BMDC maturation, BMDCs were pretreated with the MLKL inhibitor GW806742X (0.1, 0.5, 1 µM) for 1 h prior to LPS stimulation for 24 h. As shown in Fig. 3A, BMDCs viability was not affected at any concentration tested. Accordingly, subsequent analyses were performed on viable BMDCs population to determine the effect of MLKL inhibition on maturation. As expected, LPS stimulation significantly induced BMDC maturation, characterized by increased expression of CD86, MHC-II, and PD-L1 (Fig. 3B-D). Notably, MLKL inhibition significantly reduced the expression of these maturation markers in a dose-dependent manner, with the strongest effect observed at 1 µM (Fig. 3B-D), indicating inhibition of MLKL impairs BMDC maturation.

Fig. 3.

MLKL inhibition impairs BMDC maturation and reduces BMDC-mediated CD8⁺ T cell priming. (A) Percentage of BMDCs viability following treatment with MLKL inhibitor; GW806742X (0.1, 0.5, 1 µM) prior to LPS stimulation, as analyzed by flow cytometry. Effect of MLKL inhibition on BMDC maturation, shown as the percentage of cells expressing the maturation markers (B) CD86 (C) MHC-II (D) PD-L1, as analyzed by flow cytometry. (E) Schematic illustration of the experimental workflow for in vitro DC-CD8⁺ T cell priming assay. (F) Representative CFSE dilution histograms showing OT-I CD8⁺ T cell proliferation following co-culture with OVA-loaded BMDCs. Proliferation was analyzed within the live OT-I CD8⁺ T cell population based on CFSE dilution, and only divided daughter cell generations were included in the proliferation analysis. (G) Quantification of OT-I CD8⁺ T cell proliferation expressed as the percentage of divided cells

Given that BMDC maturation is critical for antigen presentation and Tcell priming, we next examined whether MLKL inhibition in BMDCs affects the ability of BMDCs to prime CD8⁺ T cells. Immature BMDCs were incubated with ovalbumin (OVA) as a model antigen in the presence or absence of the MLKL inhibitor and subsequently co-cultured with CFSE-labeled naïve OT-I CD8⁺ T cells, which express a transgenic Tcell receptor specific for the OVA peptide at a BMDCs:CD8⁺ T cells ratio of 1:5 for 72 h (Fig. 3E). As expected, OVA-loaded BMDCs induced OT-I CD8⁺ T cell proliferation, as indicated by CFSE dilution. In contrast, BMDCs pretreated with MLKL inhibitor significantly reduced CD8⁺ T cell proliferation compared with vehicle controls (Fig. 3F-G). Collectively, these findings suggest that MLKL may play a role in BMDC maturation and BMDC-mediated CD8⁺ T cell priming.

MLKL inhibition reduces CD8⁺ T cell activation, proliferation, and cytotoxic function

Following our observation that MLKL regulates BMDC maturation and BMDC-mediated CD8⁺ T cell priming, we next sought to determine whether MLKL also exerts an immune-intrinsic role in CD8⁺ T cells. This rationale was supported by our transcriptomic analyses and expression studies demonstrating that MLKL is broadly expressed in immune cells and is upregulated following Tcell activation. Because the immune-intrinsic function of MLKL in CD8⁺ T cells remains largely unexplored, we investigated whether MLKL directly regulates CD8⁺ T cell activation, proliferation, and effector function.

Naïve CD8⁺ T cells were pretreated with the MLKL inhibitor GW806742X (0.1, 0.5, 1 µM) for 1 h prior to TCR stimulation with anti-CD3 and anti-CD28 antibodies for 48 h. As shown in Fig. 4A, GW806742X did not significantly affect the viability of activated CD8⁺ T cells at any concentration tested. Therefore, all subsequent analyses were performed on live CD8⁺ T cells. As expected, TCR stimulation significantly increased the expression of activation markers and effector molecules in CD8⁺ T cells. However, inhibition of MLKL led to a dose-dependent reduction of CD8⁺ T cell activation, as shown by decreased expression of CD44, PD-1, IFN-γ and perforin compared with activated controls (Fig. 4B-E). Consistently, CD8⁺ T cell proliferation, assessed by CFSE dilution, was also significantly reduced following MLKL inhibition (Fig. 4F). To determine whether these functional impairments translate into reduced anti-tumor activity, we next performed a tumor killing assay. Activated CD8⁺ T cells with or without 1 µM MLKL inhibitor, the most effective concentration in inhibiting CD8⁺ T cell function, were co-cultured with B16-OVA melanoma cells at varying effector-to-target (E:T) ratios. Tumor cell viability was assessed by flow cytometry using live/dead staining, while CD8 staining was used to distinguish effector CD8⁺ T cells from tumor cells (Fig. 4G). As expected, activated CD8⁺ T cells efficiently reduced tumor cell numbers. In contrast, inhibition of MLKL in CD8⁺ T cells significantly increased the number of viable tumor cells, indicating impaired cytotoxic activity (Fig. 4H). To genetically validate the pharmacological findings, we performed pilot CRISPR/Cas9 RNP-mediated Mlkl editing in BMDCs and CD8⁺ T cells using two independent Mlkl-targeting sgRNAs (Supplementary Methods). However, efficient and reproducible MLKL depletion was not achieved, and RNP transfection reduced cell viability following immune activation, precluding downstream functional analyses (Supplementary Figs. 7–8). Further optimization of genetic approaches will therefore be required to confirm the immune-intrinsic functions of MLKL. Taken together, these pharmacological findings suggest that MLKL may regulate CD8⁺ T cell activation, proliferation, and cytotoxic function, highlighting its potential role in regulating CD8⁺ T cell-mediated anti-tumor immunity.

Fig. 4.

MLKL inhibition reduces CD8⁺ T cell activation, proliferation, and cytotoxic function. (A) Percentage of CD8⁺ T cells viability following treatment with MLKL inhibitor GW806742X  (0.1, 0.5, 1 µM) prior to TCR stimulation with anti-CD3 and anti-CD28 antibodies for 48 h, as analyzed by flow cytometry. Effect of MLKL inhibition on CD8⁺ T cell function shown as the percentage of cells expressing (B) CD44 (C) PD-1 (D) IFN-γ (E) perforin-1 and (F) CFSE, as analyzed by flow cytometry. (G) Schematic illustration of the experimental workflow for tumor killing assay. (H) Bar graph showing the absolute number of live tumor cells following co-culture with CD8⁺ T cells treated with or without 1 µM GW806742X at different effector-to-target (E:T; OT-I: B16-OVA) ratios for 72 h

In silico analysis of MLKL associations in cancer patient cohorts receiving immune checkpoint blockade (ICB)

Building on our promising findings from in vitro experiments in mouse-derived immune cells, which indicate that MLKL may regulate immune cell function and contribute to key processes underlying effective anti-tumor immunity, we next sought to move closer to clinical translation by integrating publicly available datasets from cancer patient cohorts. We analyzed eight quality-checked, preprocessed scRNA-seq datasets encompassing nine cancer types treated with ICB [31] to examine MLKL expression across immune cell populations and compare its expression patterns between responders and non-responders. Across the integrated scRNA-seq datasets, MLKL expression was detected in multiple immune and stromal cell populations, including T cells, DCs, and macrophages (Fig. 5A-B). Within the Tcell compartment, MLKL expression appeared enriched in discrete subpopulations rather than being uniformly distributed (Supplementary Fig. 9A-B), suggesting a potential association with specific T cell functional states. To further characterize the T cell states associated with MLKL expression, we first examined canonical markers of naïve-like, activation, and effector programs across annotated Tcell subsets. Naïve-like T cell populations showed enrichment of TCF7 and SELL, whereas activated and effector CD8⁺ T cell subsets showed higher expression of activation- and effector-associated genes, including PDCD1, IL2RA, TNF, IFNG, PRF1, GZMB, and CD69 (Supplementary Fig. 9C). We then examined MLKL expression across the same Tcell subsets and found that MLKL was relatively enriched in activated/effector-like CD8⁺ T cell populations compared with naïve-like Tcell populations (Fig. 5C). Following ICB treatment, activated CD8⁺ T cells became more prominent, whereas naïve-like Tcell populations were reduced (Supplementary Fig. 9D). These observations are consistent with our in vitro findings in mouse T cells, where MLKL expression increased upon activation, and further support the idea that MLKL is linked to activated Tcell programs relevant to anti-tumor immunity. In addition, donor-level comparison of responder and non-responder samples showed significantly higher MLKL expression in Tcell populations from favourable responders compared with unfavourable responders (p = 0.027) (Fig. 5D). Moreover, MLKL expression was also enrichment in conventional DCs. Similar to the Tcell compartment, MLKL expression in these DCs subsets also tended to be higher in responder-associated populations (Supplementary Fig. 9F-G). Together, these findings suggest that the clinical relevance of MLKL may be context-dependent, with higher expression in immune cells, particularly T cells, potentially associated with favorable clinical responses to ICB.

Fig. 5.

In silico analysis of MLKL associations in cancer patient cohorts receiving ICB. (A) UMAP plot showing the 33 identified cell clusters obtained from integrated scRNA-seq datasets of cancer patients receiving ICB. (B) UMAP plot showing the distribution of MLKL expression across the identified cell populations. (C) Donor-level pseudobulk MLKL expression across distinct CD8⁺ T cell subsets. (D) Donor-level pseudobulk MLKL expression in T cell populations from patients with favourable and unfavourable responses to ICB. Each dot represents an individual donor

Discussion

In this study, we demonstrate that MLKL is dynamically upregulated during immune activation across multiple immune cell subsets. Pharmacological inhibition of MLKL impaired BMDC maturation, reduced BMDC-mediated CD8⁺ T cell priming, and suppressed CD8⁺ T cell activation, proliferation, and cytotoxic function. Supporting these experimental findings, integrated scRNA-seq analyses from cancer patients receiving immune checkpoint blockade (ICB) revealed enrichment of MLKL expression in activated and effector CD8⁺ T cell populations associated with clinical response. Collectively, these findings support an immune-intrinsic role for MLKL in dendritic cells (DCs) and CD8⁺ T cells and suggest that MLKL may contribute to effective anti-tumor immune responses.

Our findings that MLKL expression is upregulated during immune activation are consistent with previous studies across multiple immune cell types. In BMDMs, MLKL mRNA and protein expression are predominantly induced under M1-polarizing conditions (IFN-γ plus LPS), but not under M2 conditions [33, 34]. Similarly, BMDCs upregulate MLKL following LPS-induced maturation [33], suggesting that MLKL is an inflammation responsive molecule whose expression is induced during pro-inflammatory activation. Because MLKL expression in BMDMs was assessed only at the endpoint of macrophage polarization in the present study, future investigations examining the temporal kinetics of MLKL induction during macrophage polarization may provide further mechanistic insight into its regulation.

Large-scale transcriptomic analyses have shown that MLKL mRNA expression is highly inducible under inflammatory conditions, whereas RIPK3 expression remains largely unchanged [35]. As MLKL has been identified as an interferon-stimulated gene (ISG), with its expression being induced by inflammatory stimuli through the IFN/JAK/STAT signaling pathway, our findings raise the possibility that IFN signaling may contribute to the regulation of MLKL expression in immune cells during immune activation [34–37]. Although RIPK3 expression also increased following immune activation in our experimental system, MLKL exhibited a more pronounced transcriptional response. We further analyzed the expression patterns of RIPK1 and RIPK3 in our scRNA-seq datasets using the same donor-level analytical framework applied to MLKL. Unlike MLKL, neither RIPK1 nor RIPK3 showed a significant association with ICB response across the immune compartments analyzed (Supplementary Fig. 10A-B). These findings suggest that the responder-associated expression pattern observed in the human datasets was more evidence for MLKL than for other core components of the necroptosis pathway, further supporting our focus on the immune-intrinsic role of MLKL in anti-tumor immunity. Nevertheless, the absence of a significant association does not exclude potential immune-intrinsic functions of RIPK3, which remain of considerable interest and warrant further investigation.

Interestingly, despite the marked induction of MLKL expression during immune activation, canonical MLKL phosphorylation was not readily detected in activated BMDCs, BMDMs, or CD8⁺ T cells under our experimental conditions, and only weak phosphorylation was observed in activated CD4⁺ T cells. These findings suggest that increased MLKL expression during immune activation does not necessarily coincide with activation of the canonical RIPK3-MLKL necroptosis pathway. Instead, MLKL may regulate immune cell function through non-necroptotic mechanisms, consistent with accumulating evidence that MLKL regulates inflammatory signaling, vesicle trafficking, and other cellular processes independently of necroptotic cell death [17–22]. However, the molecular mechanisms underlying these non-necroptotic functions of MLKL remain unclear and warrant further investigation.

DCs are essential for initiating adaptive immune responses through antigen presentation and Tcell priming [1, 3, 38]. Previous studies in autoimmune models have demonstrated that MLKL deficiency in BMDCs enhances CD4⁺ T cell proliferation independently of RIPK3. In contrast, our findings show that pharmacological inhibition of MLKL in BMDCs reduces CD8⁺ T cell proliferation, indicating impaired priming capacity. Together with a recent study showing that MLKL promotes DC maturation under caspase-inhibited conditions via functions beyond necroptosis [26], these findings support a context-dependent role for MLKL in regulating DC maturation and Tcell priming. Interestingly, MLKL inhibition produced a more pronounced impairment in CD8⁺ T cell priming than in BMDC maturation marker expression, suggesting that surface maturation markers alone may not fully reflect the functional competence of DCs. Given that MLKL has been implicated in inflammasome activation, MyD88/NF-κB signaling and membrane protein trafficking including PD-L1 trafficking [21], MLKL may promote DC maturation by coordinating inflammatory signaling and the surface expression of immune regulatory molecules required for effective Tcell priming. In addition, recent studies have identified mitochondrial fitness as a critical regulator of DC-mediated anti-tumor immunity [39]. Although the role of MLKL in DC mitochondrial function remains unknown, activated MLKL has been implicated in multiple aspects of mitochondrial biology, including ion influx, reactive oxygen species generation, and mitophagy, primarily in the context of necroptosis [40–42]. More recently, MLKL has also been shown to regulate mitochondrial function and metabolism independently of necroptosis in hematopoietic stem cells [43]. Although these findings were not obtained in DCs, they suggest that mitochondrial regulation may represent a potential mechanism through which MLKL influences DC function. However, these mechanisms were not directly investigated in the present study and warrant further investigation.

In contrast to DCs, the role of MLKL in T cells remains poorly understood, as previous studies have primarily focused on necroptosis or its upstream regulators, RIPK1 and RIPK3 [44]. In this study, we found that MLKL was markedly upregulated following TCR stimulation in both CD4⁺ and CD8⁺ T cells. Given that TCR activation promotes cytokine production, including IFN-γ [45, 46]. MLKL upregulation may be mediated through autocrine or paracrine IFN/JAK/STAT signaling, particularly via STAT1, which has been reported to regulate MLKL transcription [34, 36]. While the underlying mechanisms regulating MLKL expression across different immune cell subsets remain to be determined, pharmacological inhibition of MLKL impaired CD8⁺ T cell activation, proliferation, and cytotoxic function, supporting an immune-intrinsic role for MLKL in Tcell responses. Potential mechanisms include the regulation of membrane-associated processes, such as receptor organization and vesicle trafficking, which are critical for effective Tcell activation [17, 18, 21, 22]. In addition, MLKL may regulate intracellular signaling pathways downstream of TCR activation, including NF-κB, MAPK, and PI3K/AKT pathways, which are essential for Tcell proliferation and effector function [46]. Although direct evidence in T cells remains limited, MLKL has been implicated in inflammatory signaling [47–49], as well as ion flux and membrane permeability in other cell types [18, 50], raising the possibility that similar mechanisms may contribute to calcium-dependent signaling during Tcell activation. However, these mechanisms remain to be experimentally validated.

MLKL has emerged as a potential therapeutic target in multiple cancer types due to its role in tumor progression [16, 22, 51, 52], prompting the development of MLKL-targeted inhibitors and degraders [23]. However, our findings highlight that MLKL also contributes to immune cell function, particularly in DCs and CD8⁺ T cells. Consistent with this concept, our integrated scRNA-seq analyses from cancer patients receiving ICB demonstrated that MLKL expression was enriched in activated and effector CD8⁺ T cell populations and associated with clinical response. Together, these findings support context-dependent roles of MLKL across tumor and immune compartments and suggest that systemic MLKL inhibition may compromise anti-tumor immunity while targeting tumor cells. Similar context-dependent effects have been described for the JAK/STAT pathway, where targeted therapeutic strategies have been developed to maximize anti-tumor efficacy while minimizing disruption of beneficial immune responses [53–55]. Therefore, understanding the distinct function of MLKL in tumor and immune compartments will be important for the rational design of MLKL-targeted therapeutic strategies.

Although these findings provide new insights into the immune-intrinsic role of MLKL, several limitations should be acknowledged. First, a highly specific murine MLKL inhibitor is currently unavailable. Moreover, GW806742X has been reported to also target RIPK1 and VEGFR2 [56] and therefore its effect may not exclusively reflect MLKL-specific inhibition. Although GW806742X did not cause substantial overall cell death and canonical MLKL activation was not detected, Live/Dead staining cannot distinguish between specific modes of cell death. Thus, a contribution from apoptosis or other forms of cell death cannot be completely excluded. Future studies using cell-death-specific assays and genetic approaches, such as knockdown, knockout, or overexpression models, will be important to validate the immune-intrinsic functions of MLKL. Second, our DC experiments were performed using GM-CSF-derived BMDCs, which may not fully recapitulate the complexity and heterogeneity of primary DC subsets in vivo [57]. Consistent with this notion, our integrated scRNA-seq analyses demonstrated that MLKL expression varies across distinct DC and myeloid cell populations, suggesting that its immune functions may be subset- and context-dependent. Future studies using purified primary DC subsets, such as conventional DCs (cDCs), as well as distinct macrophage populations, will be important to further define the physiological relevance of these findings. Third, although MLKL inhibition produced clear functional effects in both DCs and CD8⁺ T cells, the underlying molecular mechanisms were not directly investigated. Future studies examining membrane trafficking, intracellular signaling and mitochondria regulation will be necessary to elucidate how MLKL regulates immune cell function. Lastly, as the functional experiments were performed primarily in vitro, further studies using in vivo models and human primary immune cells will be required to determine the physiological relevance of MLKL in regulating anti-tumor immunity within the TME. In particular, comprehensive spectral flow cytometric profiling of tumor-infiltrating Tcell subsets, together with the assessment of functional cytokines such as IFN-γ and TNF-α following MLKL inhibition, will be important for defining the contribution of MLKL to distinct Tcell populations.

Conclusions

Our findings show that MLKL is upregulated during immune activation and contributes to dendritic cell maturation and CD8⁺ T cell activation, proliferation, and cytotoxic function. Pharmacological inhibition of MLKL impairs these immune functions, supporting an immune-intrinsic role for MLKL in DCs and CD8⁺ T cells. Together with the integrated scRNA-seq analyses, our findings highlight the importance of considering the distinct functions of MLKL in both tumor and immune compartments when developing MLKL-targeted therapeutic strategies, as systemic MLKL inhibition may influence anti-tumor immunity.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

BMDCs

Bone marrow-derived dendritic cells

BMDMs

Bone marrow-derived macrophages

CFSE

Carboxyfluorescein succinimidyl ester

DCs

Dendritic cells

ICB

Immune checkpoint blockade

MLKL

Mixed lineage kinase domain-like protein

OVA

Ovalbumin

RIPK3

Receptor-interacting protein kinase 3

RT-qPCR

Reverse transcriptase-quantitative polymerase chain reaction

TCR

T-cell receptor

TME

Tumor microenvironment

Author contributions

ND: Methodology, Validation, Investigation, Visualization, Formal Analysis, Writing-Original Draft. TL: Methodology, Investigation, Conceptualization, Formal Analysis, SH: Methodology, Visualization FG: Resources, provided instrumentation, animal mouse models, animal ethics, experimental protocols, Supervision, Writing-Review & Editing SJ: Conceptualization, Supervision, Funding Acquisition, Validation, Formal Analysis, Writing-Review & Editing.

Funding

This study was supported by the Office of National Higher Education Science Research and Innovation Policy Council by Program Management Unit for Human Resources and Institutional Development, Research and Innovation (B05F640067, to S.J.), the Royal Golden Jubilee Ph.D. Programme Scholarship from National Research Council of Thailand (NRCT) (N41A670190) and the 90^thAnniversary Chulalongkorn University Fund (Ratchadaphiseksomphot Endowment Fund) (GCUGR1125672067D, to N.D.). F.S.G. was funded by the National Cancer Institute 1K22KCA258960-01, Victoria’s Secret Global Fund for Women’s Cancers Career Development Award in Partnership with Pelatonia and American Association for Cancer Research, Cancer Research Institute Clinical Laboratory Integration Program, and the Krantz Spark Award.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declaration

Ethical approval

All animal experimental procedures and research protocols were approved by the Institutional Animal Care and Use Committee (IACUC; 2022N000118), Mass General Brigham, Charlestown, Massachusetts, USA, and were conducted in accordance with institutional and national guidelines for the care and use of laboratory animals.

Competing interests

The authors declare no competing interests.