Decoding necrosome assembly: harmonizing signal amplification and attenuation through optimal RIP3 stoichiometry

Domain: physics
Published: 2025-12-23
Editorial outcome: major_revision

Abstract
Necrosome assembly is essential for necroptosis, a process implicated in neurodegeneration, ischemic injury, and inflammatory diseases. Yet the spatiotemporal rules governing this assembly remain elusive. Leveraging quantitative STORM and mathematical modeling, we define an approximately 3:1 ratio of RIP3 to RIP1 in necrosomes as the optimal stoichiometry for necroptosis, enabling signal amplification and a threshold response. Surprisingly, excessive RIP3 oligomerization attenuates signaling, acting as an intrinsic size control mechanism. RIP3 assembly is dynamically regulated: it is constrained by stimulation and RIP1, promoted by RIP3 itself, and unexpectedly limited by downstream MLKL. A complementary balance between necrosome quantity and RIP3 assembly degree ensures efficient MLKL phosphorylation. In contrast, Caspase-8 assembly is limited by c-FLIP and recruited linearly by RIP1, while its distinct behavior from RIP3 underlies the biphasic necroptotic response to RIP1. These findings uncover the flexible, multi-strategic nature of signalosomes and offer valuable insights for therapeutic and synthetic biology.

Introduction
With the growing recognition of structural diversity of signalosomes, higher-order assemblies have emerged across a wide spectrum of biological landscapes, generally involving the polymerization of receptors, adaptors, and effectors into large structures with defined, rigid shapes or dynamic, liquid-like condensates1–3. These supramolecular complexes enhance signal transduction, amplify cellular responses, and facilitate enzyme activation, while maintaining a dynamic and tightly regulated structure4–6. Among them, amyloid-like assemblies are a well-known subclass, essential for the assembly of RIP1-RIP3 in necrosomes7.

Necrosome assembly, the central signaling event driving necroptosis, has been primarily studied in the context of TNF signaling8. Upon TNF treatment and inhibition of apoptosis, a cascade of events triggers the assembly of necrosomes (Fig. 1a). TNF receptor 1 (TNFR1) recruits TNF receptor-associated death domain (TRADD) and RIP1, which subsequently dissociates from the membrane-bound complex and interacts with RIP3. Our prior research indicates that TRADD does not participate in necrosomes formation9, while MLKL is additionally recruited into necrosomes. The RIP homology-interacting motif (RHIM) domains of RIP1 and RIP3 form filamentous structures reminiscent of β-amyloids. The structural organization is intricate, with both RIP1 and RIP3 adopting a meandering β-strand architecture, comprising four short β-segments separated by turns, which collectively form the serpentine fold. The hetero-amyloid complex formed by RIP1–RIP3 serves as a nucleation site for RIP3 recruitment and polymerization via the same β-amyloid interaction10. Meanwhile, RIP1 utilizes its death domains (DD) to recruit FAS-associated death domain (FADD), which further interacts with caspase-8 via the death effector domain (DED). DED-filaments serve as scaffolds for caspase-8 clustering onto FADD, resulting in the formation of a supramolecular complex11,12. Upon activation, RIP3 phosphorylates its substrate, mixed-lineage kinase domain-like protein (MLKL). Our recent study suggests that RIP3 oligomers, particularly those sized tetramer or above13, facilitate MLKL polymerization within necrosomes14, driving membrane permeabilization and subsequent necroptotic cell death15–19.

Necrosome is a supramolecular signaling complex that serves as the central execution module of necroptosis. Necrosome assembly not only ensures the elimination of infected or damaged cells but also promotes the release of damage-associated molecular patterns (DAMPs), thus amplifying inflammatory responses20. Importantly, aberrant necrosome assembly or dysregulation of its components has been implicated in a wide spectrum of pathological conditions, including ischemia-reperfusion injury, neurodegenerative diseases, inflammatory bowel disease, and certain types of cancer21. As such, dissecting the principles of necrosome assembly is critical for understanding the molecular logic of necroptosis and for identifying therapeutic targets in inflammation-associated diseases. Yet, decoding the spatial organization and functional logic of necrosomes remains challenging.

Traditional approaches such as immunoprecipitation-mass spectrometry (IP-MS) provide ensemble-level stoichiometry but average out nanoscale heterogeneity. Single-molecule localization microscopy (SMLM), including stochastic optical reconstruction microscopy (STORM), enables nanoscale visualization of signalosomes, but technical uncertainties such as dye photo-physics and antibody variability hinder absolute molecule counting22. Therefore, our understanding of necrosome assembly is far from complete: Why do RIP1 and RIP3 form such specific configurations in necrosomes? How do cells terminate signaling from these higher-order assemblies? What controls the size of these higher-order complexes, and are there specific or universal mechanisms underlying this process?

In this study, we applied an integrative strategy combining super-resolution STORM imaging, mass spectrometry, and mathematical modeling. By introducing an internal MAP7-based calibration system and an SMAP–POCA analysis pipeline, we overcame technical uncertainties in STORM quantification. We discovered that necrosomes adopt an optimal stoichiometry, with approximately three RIP3 molecules per RIP1, balancing signal amplification and attenuation. Notably, larger RIP3 assemblies suppress necroptotic signaling under MLKL-mediated negative feedback, extending current understanding of higher-order complexes as purely amplifying platforms. Caspase-8 assembly is limited by c-FLIP and recruited linearly by RIP1, while its distinct behavior from RIP3 underlies the biphasic necroptotic response to RIP1. This flexible, multi-strategic assembly of necrosomes offers valuable insight into the regulatory logic of cell death signaling and therapeutic modulation.

Results
Higher-order assemblies of necrosomes are a hallmark of necroptotic cells
Higher-order structures are prevalent in signal transduction and often regulates signaling fidelity and amplification through polymerization of signaling components23. Prior studies primarily examined the effects of higher-order assembly of individual proteins within complexes on signal transmission, while the cooperative assembly of multiple distinct proteins into supramolecular complexes remains less well characterized24–26. Necrosomes offer a representative model system for investigating how higher-order assemblies involving two or more distinct proteins within a complex influence signal transduction. To confirm and analyze the structure of necrosomes, we reconstituted MLKL-knockout (KO) HeLa cells with MLKL-Flag and also ectopically expressed HA-RIP3 in this cell line. Cells underwent necroptosis upon treatment with TNF, Smac mimetic, and zVAD (TSZ), as indicated by robust phosphorylation of RIP3 and MLKL (Fig. 1b, left panel). Notably, RIP1, RIP3, and MLKL formed SDS-resistant, high-molecular-weight polymers that accumulated at the top of the non-reducing gel upon long exposure, consistent with their incorporation into supramolecular structures (Fig. 1b, right panel). These polymeric assemblies were also observed in various cell lines, including human HT-29 and murine L92913, indicating a conserved necrosomes polymerization process across species. To visualize necrosome formation, we performed confocal microscopy in HeLa cells treated with DMSO or TSZ. In TSZ-treated cells, RIP1, RIP3, and MLKL formed distinct punctate structures, in contrast to their diffuse localization in control cells (Fig. 1c). These puncta frequently co-localized, as highlighted in the boxed regions, illustrating that higher-order assembly serves as a general and crucial step for necroptosis induction.

Necrosome stoichiometry characterizes the nanoscale assemblies of RIP1, RIP3, MLKL, and caspase-8
To map the nanoscale architecture of necrosome, we employed super-resolution microscopy (STORM) with a lateral localization precision of ~12.93 nm, calculated by the LAMA algorithm27. As shown in the workflow (Fig. 2a), we performed single-color STORM imaging of RIP1, RIP3, and MLKL in necroptotic HeLa cells reconstituted with MLKL-Flag and HA-RIP3. To quantitatively assess these supramolecular structures, we implemented a robust SMAP+POCA analysis pipeline (Supplementary Fig. 1, see the Methods section for details), incorporating Voronoi-based clustering and morphological filtering. In untreated cells, RIP1, RIP3, and MLKL were visualized as dispersed small puncta in cytosol (TSZ 0h, Fig. 2b). After stimulation, RIP1, RIP3 and MLKL formed larger punctate structures in round or rod shapes at early time (TSZ 2h, Fig. 2b). These puncta grew and exhibited rod-shaped structures during necroptosis after long-time TSZ treatment (TSZ 4h, Fig. 2b). To analyze the rod-shaped structures of RIP1, RIP3 and MLKL, we quantified the area of STORM-identified puncta (Fig. 2c). For RIP1 rods, the predominant area was ~17×10³ nm² at early stages (TSZ 2h), with maximum values up to ~39×10³ nm². At later time points (TSZ 4h), the predominant area increased to ~20×10³ nm², with maxima up to ~51×10³ nm². RIP3 rods exhibited larger areas than RIP1, with a predominant value of ~42×10³ nm² and maxima of ~103×10³ nm² at 4h. MLKL rods showed areas similar to RIP1 rods during TSZ treatment. These findings indicate that RIP3 contributes more substantially than RIP1 to rod-shaped necrosome assembly, consistent with previous reports showing that additional RIP3 homopolymerization onto the RIP1–RIP3 hetero-amyloid platform is critical for necroptosis induction13. Notably, the temporal increase in the fraction of RIP1-, RIP3-, or MLKL-positive rod-shaped necrosomes reflects a structural maturation process, transitioning from early oligomeric puncta to stabilized, rod-like supramolecular complexes during necroptosis (Fig. 2d).

To accurately determine necrosome stoichiometry, we utilized immunoprecipitation (IP) coupled with quantitative mass spectrometry (MS). Heavy amino acid-labeled proteins were spiked into purified IP complexes (FLAG-RIP3 and FLAG-MLKL), which were subsequently subjected to SWATH-MS and DDA analyses, allowing quantitative identification of necrosome-associated components across multiple time points (Fig. 2e). A substantial quantity of necrosome-associated proteins was detected, with our primary focus on elucidating the stoichiometry of key signal transducers (Supplementary Fig. 2). Through immunoprecipitation of RIP3 complex, we demonstrated that the ratio of caspase-8 to RIP1 was approximately 1:1 at early time (2h) but gradually decreased over time (Fig. 2f), implying the presence of more than one RIP1 per caspase-8 within necrosomes at late stages. Similarly, the MLKL to RIP1 ratio was initially around 1:1 but increased to approximately 2:1 in the late stage (4h–8h). In MLKL co-immunoprecipitation assays, the RIP3 to RIP1 ratio stabilizes at about 3:1 during the late stage. The consistent ratios of RIP3 to RIP1 and MLKL to RIP1 at late stage suggest that necrosomes adopt a stable assembly mode to sustain necroptotic signaling.

Based on these quantitative measurements, we deduced an in situ necrosome stoichiometry of RIP1, caspase-8, RIP3, and MLKL approximately 2:1:6:4 in late-stage necroptosis (Fig. 2g). This assembly ratio outlines an assembly subunit that reflects a mechanistically optimized stoichiometry of the necrosome: two RIP1 molecules together with two RIP3 molecules form a heterotypic RHIM ‘seed’ (PDB: 5V7Z)10; among six RIP3 molecules, four assemble into a RIP3 tetramer, the minimal execution unit of necroptosis (PDB: 7DAC)28, which activates MLKL (PDB: 7MON) and thereby drives four MLKL monomers to form the functional MLKL tetramer29. Caspase-8 participates as a non-catalytic monomeric regulator (PDB: 8YD8)30. Together, these features are consistent with prior reports and represent a structurally and functionally coherent scheme for necrosome assembly.

A three-to-one or higher RIP3:RIP1 stoichiometry shapes signal amplification and threshold response
The precise mechanism by which the identified necrosome stoichiometry (Fig. 2g) governs subsequent activation remains unclear. To address this, we developed a basic mathematical model comprising two constituents, A and B, representing upstream and downstream signals, respectively (Fig. 3a and Supplementary Table 1). Within the binding complex, B is recruited and activated by A, and both A and B can form various higher-order assemblies. m and n represent the numbers of A and B molecules in the binding complex. We first considered the scenario where A remains monomeric (m=1), while B assembles into different higher-order structures (Fig. 3b, upper panel). Compared to the monomeric B (n=1, black line), dimeric (n=2, red line) or tetrameric (n=4, blue line) B substantially amplifies output signal, as measured by the level of activated B. Additionally, the input-output relationship transitions from a linear pattern when B is monomeric to nonlinear patterns as B assembles into dimeric or tetrameric structures. These findings align with the prevailing view that higher-order assembly is a pivotal initial step in achieving signal amplification and threshold behavior1,2. Next, we discussed the inverse condition, where B remains monomeric (n=1) while A forms higher-order structures. In this case, increasing the assembly order of A (m=2, 4) considerably reduces output signal compared to monomeric A (m=1) (Fig. 3b, lower panel). Moreover, as A transitions from a monomeric to dimeric or tetrameric structure, the response pattern of output to input becomes increasingly linear. A more comprehensive analysis considering various stoichiometries of A and B delineates the boundaries of signal control and response characteristics (Fig. 3c). As shown in our results, when B molecules outnumber A (n>m), signal amplification (upper panel) and nonlinear response (lower panel) predominate. Conversely, when A molecules outnumber B (n<m), signal attenuation and linear response behavior emerge. The greater the disparity between m and n, the more pronounced the corresponding output behaviors become.

To assess the functionality of rod-shaped RIP1-RIP3 complexes, we also developed a comprehensive protein interaction model of TNF-induced necroptotic signaling (Fig. 3d, Supplementary Fig. 3, and Supplementary Table 2). Given the quantified RIP3-to-RIP1 ratio of approximately 3:1 (Fig. 2f), we implemented a binding rule where three RIP3 molecules interact with one RIP1 molecule. In necrosomes, caspase-8 safeguards cells against necroptosis through inactivating RIP1 and RIP331,32. To eliminate the interference of caspase-8, we calibrated the model using experimental data of RIP3 phosphorylation obtained with TSZ treatment (Fig. 3d, lower panel). We then varied the number of RIP3 molecules to examine how different stoichiometric ratios of RIP3 to RIP1 influence RIP3 activation. Simulations showed that a subunit composed of a single RIP3 molecule (n=1) and one RIP1 molecule (m=1), i.e., a 1:1 RIP3:RIP1 stoichiometry, fails to induce RIP3 phosphorylation (Fig. 3e), corroborating previous experimental data showing that a RIP1-RIP3 heterodimer alone is insufficient to trigger necroptosis13,33. Likewise, a subunit containing two RIP3 molecules (n=2, m=1), corresponding to a 2:1 RIP3:RIP1 stoichiometry, barely induces RIP3 phosphorylation. Only oligomers with at least three RIP3 molecules per one RIP1 (n=3 or greater), corresponding to a RIP3:RIP1 ratio of three to one or higher, effectively promote extensive phosphorylation (Fig. 3e). Thus, the 3:1 stoichiometric ratio of RIP3 to RIP1 defines the threshold size for initiating necroptosis. The higher abundance of RIP3 relative to RIP1 within necrosomes serves as an effective mechanism for signal amplification. As an inherent property of supramolecular complexes, RIP3 stoichiometry also elicits a threshold response in necroptotic signaling. Simulations demonstrated that when a 3:1 stoichiometric ratio is present, RIP3 phosphorylation remains relatively stable as RIP1 levels decrease from 100% to 20%, but drops sharply below this threshold (Fig. 3f, upper panel). This threshold behavior was experimentally validated in our previous study9, showing shRNA-mediated RIP1 knockdown scarcely impacted RIP3 phosphorylation determined by immunoblot densitometry, whereas RIP1 depletion abolished RIP3 phosphorylation (Fig. 3f, lower panel).

To experimentally validate the model predictions with reliable quantitative STORM, we designed a microtubule-associated protein MAP7-based calibration system. We introduced either a Flag-MAP7-HA or a dual-tagged 2×Flag-MAP7-HA construct (via a P2A system) into HeLa cells, ensuring defined antigen stoichiometry (Fig. 3g). Confocal microscopy confirmed robust labeling and canonical microtubule morphology in both constructs (Fig. 3h). Quantification of fluorescence intensity showed an expected ~2-fold increase in Flag:HA signal in 2Flag-MAP7-HA cells compared to Flag-MAP7-HA (1.93×; Fig. 3i). Subsequent STORM imaging revealed that the average localization ratio of Flag to HA closely matched the theoretical value (1.79× vs. 2.00×; Fig. 3j, k). These results demonstrate that, despite inherent uncertainties in antibody recognition and dye photophysics, the MAP7-based reference system conceptually validates the quantitative potential of antibody-based dual-color STORM when combined with high-quality reagents and calibration controls.

Using this system, we next examined RIP1 and RIP3 stoichiometry in individual necrosomes. Three-color STORM was performed in TSZ-treated RIP1-MLKL double-knockout (DKO) HeLa cells reconstituted with Flag-RIP1 and HA-RIP3 (Fig. 3l). The immunostaining procedures, antibody selection (e.g., anti-Flag and anti-HA), and imaging/analysis workflows were identical to those used in MAP7 calibration, ensuring quantitative comparability. Segmentation and overlap analysis showed a wide distribution of RIP3:RIP1 ratios across necrosomes at 4h post-TSZ, ranging from ~1.0 to ~9.0, with an average of 3.6 (Fig. 3m and Supplementary Fig. 4). To probe necrosome signaling output, we quantified RIP3 activation within each necrosome. The experimental distribution of p-RIP3 closely mirror the model prediction (Fig. 3e): p-RIP3 remains near baseline when RIP3:RIP1≤2, exhibits a sharp, switch-like rise as the ratio approaches ~3, and then plateaus near maximal levels for ≥3. This concordance demonstrates that the RIP3:RIP1 stoichiometric ratio functions as a critical determinant, triggering pronounced signal amplification and switch-like behavior in necrosome activation.

Signal attenuation serves as an intrinsic size control mechanism for RIP3 stoichiometry
Current research strongly supports the critical role of supramolecular assemblies, such as the RIP3 stoichiometry in our study (Fig. 3), in amplifying signal transduction1,2,34. However, the expansion of higher-order complexes is not limitless and must adhere to intrinsic constraints. How do cells terminate signaling from such higher-order assemblies? Specifically, once RIP3’s higher-order complex is assembled, what mechanisms control its spatial size? We expanded the basic mathematical model (Fig. 3a) to encompass three constituents: A, B, and C, representing upstream, midstream, and downstream signals, respectively (Fig. 4a and Supplementary Table 1). Upon stimulation, A recruits and activates B, which subsequently interacts with C to form a complex. Within the complex, A, B, and C are capable of forming various higher-order assemblies, with m, n, and h denoting the number of molecules of A, B, and C. We analyzed different assembly ratios to elucidate how these strategies define output responses (Fig. 4b). Three representative scenarios were selected for clarity: When both A and B are monomeric (m=1, n=1), yielding an A:B:C stoichiometry of 1:1:h, the output progressively intensifies as C’s assembly (h) transitions to higher-order assemblies (Fig. 4b, blue line, right panel). When A is monomeric (m=1) and C forms a trimer (h=3), corresponding to an A:B:C stoichiometry of 1:n:3, the output initially rises and then declines as B’s assembly degree increases, peaking when B approaches a dimer (n=2) (Fig. 4b, green line, right panel). Similarly, when B is monomeric (n=1) and C forms a trimer (h=3), giving an A:B:C stoichiometry of m:1:3, the output reaches its maximum when A approximates a dimer (m=2) (Fig. 4b, red line, right panel). Based on principles of maximum efficiency and optimal signaling, cells preferentially select the optimal ratio for efficient signal transduction.

Theoretically, the existence of the optimal stoichiometry of B can be intuitively recognized. As depicted in Fig. 4c, the three-component system can be decomposed into two two-component systems (TCS). In TCS-1, consisting of upstream A and midstream B, B functions as a downstream signal of A. Analysis of two-component systems shows that increasing B’s assembly degree enhances output signal (Fig. 3c), with B’s higher-order assembly acting as a signal amplifier. However, in TCS-2, where B is midstream and C is downstream, B serves as an upstream signal to C. Here, increasing B’s assembly degree reduces output intensity, with B’s higher-order assembly serving as an inhibitor. Thus, excessively higher-order assembly of B serves as a mechanism for signal attenuation or even termination. This attenuation restricts further incorporation of B into complex, functioning as an intrinsic spatial size control mechanism. While moderate higher-order assembly of B amplifies signal, achieving an optimal stoichiometry is essential to preclude excessive assembly that could attenuate or terminate signal, thereby ensuring efficient transmission.

To explore the regulation of the optimal stoichiometry, additional simulations were conducted. Input intensity and component levels differentially influence B’s configuration. Modulating B’s assembly degree by input and upstream A exhibit similar trends (Fig. 4d, green dotted line). At low input intensities or A levels (<~2.5 arbitrary units (a.u.)), increases in either result in higher assembly degrees of B. Conversely, at high input intensities or A levels, further increases lead to a reduction in B’s assembly degree. Increasing B’s level enhances its higher assembly degree (Fig. 4d, blue dotted line). Unexpectedly, downstream C also regulates B’s stoichiometry (Fig. 4d, red dotted line). At low C levels (<~3.0 a.u.), increases have no significant effect on B’s stoichiometry, whereas higher C levels induce lower assembly degrees of B.

To directly assess the spatial architecture of necrosomes, we performed three-color STORM imaging of HA-RIP3 and MLKL-Flag in MLKL-KO HeLa cells (Fig. 4e). These experiments revealed rod-shaped complexes with co-localization of RIP1, RIP3, and MLKL, in agreement with our single- and two-color STORM results (Figs. 2b and 3l). To evaluate the functionality of rod-shaped RIP1-RIP3-MLKL complexes, we expanded our protein interaction model to incorporate MLKL (Fig. 4f and Supplementary Table 2). Given the quantified RIP1 to MLKL ratio of 1:2 within necrosomes (Fig. 2f), we postulated that each subunit comprises one RIP1 and two MLKL molecules, establishing a baseline RIP1:RIP3:MLKL stoichiometry of 1:n:2. We altered the number of RIP3 to evaluate its impact on MLKL activation. Consistent with the two-component RIP1-RIP3 results (Fig. 3e), stoichiometries of 1:1:2 or 1:2:2 (one or two RIP3 per subunit) produced little MLKL phosphorylation (Fig. 4g). Three RIP3 per RIP1 emerged as the threshold size for significant MLKL phosphorylation. Increasing RIP3 beyond this level, yielding 1:4:2 or higher, reduced phosphorylation efficiency, indicating that over-assembly is inhibitory. Within RIP1-RIP3-MLKL complexes, RIP3 serves as the midstream signal: its higher-order assembly amplifies upstream RIP1 signaling, while simultaneously inhibiting downstream MLKL signaling. Variability exists between cells. To simulate this, we exhaustively modeled the binding/unbinding strengths of all protein-protein interactions, along with the rates of protein synthesis/degradation in TNF pathway leading to necrosome formation, within reasonable physiological parameter ranges9,35,36. We selected 104 random TNF pathway models and calculated their MLKL phosphorylation efficiency under varying RIP3 assembly ratios. The results showed significant differences in the kinetic evolution, with synchronization among models improving as the degree of RIP3 assembly increased (Supplementary Fig. 5). Statistical analysis indicated that MLKL phosphorylation efficiency peaked at a RIP3:RIP1 ratio of three to one (Fig. 4h), suggesting that this stoichiometry is prevalent in necrosomes.

To experimentally validate the optimal RIP3 stoichiometry, we conducted three-color STORM imaging in RIP1-KO HeLa cells expressing Flag-RIP1 and HA-RIP3, using monoclonal p-MLKL antibodies (Fig. 4i). Quantification of p-MLKL as a function of the RIP3:RIP1 ratio exhibits a unimodal distribution of p-MLKL, peaking at a RIP3:RIP1 ratio of ~3 (Fig. 4j). This trend closely matched our simulation (Fig. 4g, h), confirming that stoichiometric balance between RIP3 and RIP1 governs optimal necrosome activation. Together, these data uncover signal attenuation as a key regulatory mechanism that defines the spatial size and activity of RIP3 supramolecular assemblies in necroptosis.

RIP3 stoichiometry can be selectively targeted by multiple strategies
The roles of RIP1, RIP3, and MLKL in necroptosis are well-established, yet the spatial regulation of RIP3 assembly by these proteins remains unclear. We computed a two-dimensional phase diagram of MLKL activation based on the degree of RIP3 assembly within necrosomes (Fig. 5a–c). Simulation predicted that high TNF stimulation (TNF > ~50 nM) most efficiently induces MLKL phosphorylation with a 3:1 stoichiometric ratio of RIP3 to RIP1 (Fig. 5a). Low TNF stimulation requires larger assemblies. At very low TNF levels (TNF < ~5 nM), eight RIP3 per RIP1 are required for effective MLKL phosphorylation. Therefore, RIP3 stoichiometries for MLKL activation range between three and eight molecules per RIP1. Upstream RIP1 exhibits similar regulatory behavior towards RIP3 assembly, with lower RIP1 levels correlating with higher RIP3 assembly degrees (Supplementary Fig. 6). Additionally, a positive correlation exists between RIP3 levels and its assembly degrees (Fig. 5b). Higher RIP3 levels require larger assemblies (four to eight molecules per RIP1) for effective MLKL activation. As a downstream signal, elevated MLKL levels minimally affect RIP3 stoichiometry (Fig. 5c), while reduced MLKL levels necessitate larger RIP3 assemblies, indicating that MLKL limits the supramolecular assembly of RIP3.

To test these model predictions, we performed STORM imaging analysis of RIP3 assembly in necrosomes under various TNF concentrations in Flag-RIP3 HeLa cells. Immunoblotting confirmed that increasing TNF stimulation enhances RIP3 and MLKL phosphorylation (Fig. 5d, left panel). Consistently, low TNF produced fewer but larger RIP3 assemblies, whereas high TNF yielded more numerous but smaller assemblies, revealing a clear inverse relationship between TNF concentration and the degree of higher-order RIP3 assembly (Fig. 5d, right panel and 5e). Thus, higher stimulation correlates with smaller RIP3 assemblies but a larger count of necrosomes. We next employed doxycycline (Dox)-induced RIP3-WT expression to obtain varying RIP3 levels in cells (Fig. 5f, left panel). Increased RIP3 expression leads to elevated RIP3 and MLKL phosphorylation, as well as the formation of larger RIP3 assemblies (Fig. 5f, right panel). Statistical analysis reveals a substantial rise in both RIP3 assembly degree and the average count of necrosomes per cell with higher RIP3 levels (Fig. 5g). Thus, RIP3 abundance positively regulates both its own supramolecular assembly and necrosome quantity.

To assess the role of MLKL, we knocked down MLKL to different expression levels using MLKL-specific shRNA (Fig. 5h, left panel). While MLKL is not expected to influence RIP3 assembly, imaging shows that lower MLKL levels result in fewer but larger RIP3 assemblies (Fig. 5h, right panel and 5i), suggesting MLKL not only receives signals from RIP3 but also negatively regulates RIP1-RIP3 supramolecular assembly. To confirm MLKL-mediated feedback on RIP3 activation, we examined the interaction between Flag-RIP3 and HA-RIP3 in the presence or absence of overexpressed Myc-MLKL. MLKL expression reduced the amount of HA-RIP3 pulled down with Flag-RIP3, suggesting that MLKL weakens RIP3 homo-interaction and thereby inhibits RIP3 oligomerization (Fig. 5j). We further examined RIP3 fibril dynamics and necroptosis over time in MLKL-proficient and MLKL-deficient cells (Fig. 5k–m). In MLKL-deficient cells, RIP3 fibrils continued to elongate over time, but cell death remained low. In contrast, in MLKL-proficient cells, RIP3 fibril growth plateaued as p-MLKL levels increased, followed by cell death. These data demonstrate that MLKL provides negative feedback, constraining RIP3 fibril elongation and ensuring signal termination.

Our data and prior structures support a unified structural rationale for the phase behavior in Fig. 5a–c (Supplementary Fig. 7). TNF elevates RIPK1 recruitment, favoring the alternated RIPK1:RIPK3 RHIM β-spine and effectively capping or diverting RIP3 from homo-elongation, thereby shortening RIP3 filaments10. Increasing RIP3 expression follows mass-action logic and promotes RHIM-mediated axial stacking, yielding longer homo-filaments28. By contrast, higher MLKL expression correlates with shorter RIP3 filaments without a concomitant rise in MLKL phosphorylation (Fig. 5k–m). This is consistent with structural evidence that MLKL binding induces a conformationally inactive state of RIP3, reducing the availability of stacking-competent RHIM interfaces and redistributing RIP3 into smaller assemblies29,37. In summary, RIP3 supramolecular assembly is dynamically regulated by multiple factors (i.e., TNF dosage, RIP1 abundance, RIP3 expression, and MLKL levels), ensuring precise spatial control over necrosome formation and necroptotic output.

Regulatory strategies governing RIP3 stoichiometry are ubiquitous
Elucidating the general mechanisms governing the assembly of supramolecular signalosomes is of urgent importance. To assess whether RIP3 assembly strategies within necrosomes exhibit universality, we conducted a comprehensive computational analysis across all possible scenarios of TNF-induced necrosome formation, including stochastic variations in RIP3 assembly degree (Fig. 6a). We uniformly sampled 104 parameter sets within reasonable biological intervals on a logarithmic scale in the n-dimensional parameter space9,35,36. For each parameter set, we calculated the respective MLKL phosphorylation efficiency corresponding to the stochastic RIP3 stoichiometry, generating a distribution plot encompassing all 104 models across the phase plane defined by RIP3 stoichiometry and MLKL phosphorylation efficiency (Fig. 6a, right panel). Drawing from the proposition that necrosome assemblies are optimized for signal transduction, we identified the top 5% of models with the highest efficiencies and conducted statistical analyses on their corresponding average RIP3 ratios to RIP1. Subsequently, we explored the general regulatory mechanisms underlying RIP3 assembly by examining how factors such as TNF intensity and RIP3 or MLKL levels influence RIP3 stoichiometry.

The distributions of RIP3 ratio and corresponding efficiency for the top 5% of models under varying TNF stimulation intensities and RIP3/MLKL expression levels are illustrated in Fig. 6b. Statistical analysis reveals that at 10 nM TNF stimulation, RIP3 ratios are primarily distributed between 4.5 and 8, with an average value of approximately 6.2 (Fig. 6c, upper panel). Under 20 nM TNF stimulation, the ratio predominantly falls between 1.6 and 6, averaging around 4. As stimulation intensity increases, the average RIP3 ratio consistently decreases. When TNF exceeds 70 nM, the ratio is distributed between 1 to 4, with an average of approximately 3. In contrast, higher levels of RIP3 lead to a gradual increase in both the distribution range and average ratio (Fig. 6c, middle panel). At a RIP3 expression level of 10 nM, ratios range from 1 to 4, with an average of about 2.5. Conversely, at 200 nM RIP3, ratios primarily fall between 2 and 8, averaging around 5.5. The influence of MLKL on RIP3 ratio mirrors that of TNF (Fig. 6c, lower panel). At a MLKL expression level of 10 nM, RIP3 ratios range from 3 to 7.5, averaging approximately 6. However, when MLKL exceeds 50 nM, the ratios drop to between 1 and 4, with an average near 3. These statistical findings suggest that in necrosomes, TNF negatively regulates RIP3 homo-assembly, RIP3 positively regulates its own assembly, and MLKL exerts a negative effect, all through a non-specific parameter-dependent mechanism (Fig. 6d). This theoretical framework support the universality of these regulatory mechanisms across different cell types.

Besides discussing RIP3 configuration, we examined the trends in MLKL/RIP3 phosphorylation efficiency under various conditions. With escalating TNF stimulation, MLKL phosphorylation efficiency progressively rises (Fig. 6e, upper panel). RIP3 phosphorylation efficiency remains consistently high without a discernible trend, as corroborated by the quantitative experimental data illustrated in Fig. 5d (Fig. 6f, upper panel). This indicates that under low TNF stimulation, cells harbor fewer necrosomes, necessitating the formation of larger RIP3 assemblies within each necrosome to sustain a heightened overall phosphorylation level (Fig. 5e). Conversely, during heightened stimulation, the abundance of necrosomes escalates, allowing RIP3 to maintain its phosphorylation level with shorter chains. The impact of RIP3 expression on MLKL/RIP3 phosphorylation efficiency parallels that of TNF stimulation (Fig. 6e, middle panel). Higher RIP3 levels enhance MLKL phosphorylation efficiency, while RIP3 phosphorylation efficiency remains high. This aligns with the experimental data shown in Fig. 5f (Fig. 6f, middle panel), indicating that under low RIP3 expression, smaller RIP3 assemblies suffice to maintain high RIP3 phosphorylation efficiency. Conversely, at higher RIP3 levels, cells require more necrosomes and larger RIP3 assemblies to sustain this efficiency. Despite the consistently high RIP3 phosphorylation efficiency, lower RIP3 expression levels result in a smaller total amount of phosphorylated RIP3 within cells, leading to relatively lower MLKL phosphorylation efficiency. Furthermore, as MLKL expression levels increase, MLKL phosphorylation efficiency gradually decreases, while RIP3 phosphorylation efficiency remains consistently high (Fig. 6e, down panel). This trend is also evident in the experimental data from Fig. 5h (Fig. 6f, down panel). At low MLKL levels, characterized by fewer necrosomes, larger RIP3 assemblies are required to maintain a heightened overall RIP3 phosphorylation level (Fig. 5i). However, at high MLKL levels, with more necrosomes present, smaller RIP3 assemblies are adequate. Despite the constant RIP3 phosphorylation level, the limited capacity of RIP3 to activate MLKL results in decreased MLKL phosphorylation efficiency as MLKL expression levels rise. In summary, cells adaptively modulate the assembly strategy of RIP3 within necrosomes, as well as the quantity of necrosomes, to maintain efficient RIP3 phosphorylation and signal transduction under varying conditions (Fig. 6g). When stimulation or core protein levels are low, resulting in fewer necrosomes, RIP3 forms larger assemblies to ensure high-efficiency phosphorylation. Conversely, under conditions of high stimulation or elevated protein levels, where more necrosomes are present, RIP3 only needs to form smaller assemblies to maintain high phosphorylation levels.

Distinct stoichiometries of RIP3 and caspase-8 within necrosomes orchestrate a biphasic necroptotic response
Necroptotic process is blocked by caspase-8 within necrosomes through the cleavage of RIP1 and RIP331,32. The ratio of caspase-8 to RIP1 is approximately 1:1 in the early stages (Fig. 2f). To quantitatively evaluate the functionality of RIP1-FADD-caspase-8 complex, we developed a protein interaction model of TNF signaling in RIP3-depleted cells (Fig. 7a and Supplementary Table 2). Simulation results indicate that at a 1:1 ratio between caspase-8 and RIP1, the recruitment of caspase-8 into RIP1-caspase-8 complex decreases linearly as RIP1 levels diminish (Fig. 7b, upper panel). This linear response aligns with our earlier experiments, where RIP1 knockdown progressively reduced caspase-8 recruitment upon treatment (Fig. 7b, lower panel)9.

Beyond the linear/nonlinear responses elicited by higher-order assemblies, the regulatory behavior of these signalosomes is more complicated than previously thought. Caspase-8 activation exhibits a linear dependency on RIP1 levels, while RIP3 activation displays a nonlinear pattern (Figs. 3e and 7b). Within necrosomes, both RIP1-FADD-caspase-8 and RIP1-RIP3 complexes play critical roles. RIP1-mediated caspase-8 cleaves RIP3 to block necroptosis31,32, while RIP3 also suppresses caspase-8 activation38. This mutual inhibition between caspase-8 and RIP3 likely contributes to a biphasic response in downstream signaling (Fig. 7c). To examine this, incorporating this mutual inhibition into model revealed that RIP3 phosphorylation (p-RIP3) initially increases with decreasing RIP1 levels but then sharply declines with further reduction in RIP1 (Fig. 7d, upper panel). The biphasic response of p-RIP3 to RIP1 is corroborated by the paradoxical experimental observations, showing that although RIP1 is essential for necroptosis, its elevation down-regulates necroptosis (Fig. 7d, lower panel)9,39.

Our quantitative assembly analysis shows that caspase-8 occupancy on RIP1 is low (Fig. 2f), and this stoichiometric arrangement is insufficient to drive robust amplification of the apoptotic cascade in RIP3-deficient cells. Previous studies have demonstrated that loss of RIP1 predisposes cells to apoptosis, mainly because of impaired NF-κB activation and reduced expression of anti-apoptotic proteins such as c-FLIP40,41. To test whether c-FLIP functions as a gatekeeper of caspase-8 activation and aggregation, we treated HeLa cells with TNF plus cycloheximide (TC) to block protein synthesis and performed time-resolved comparisons between wild-type and RIP1-deficient cells at 0, 3, 6, and 9h (Fig. 7e–g). TC treatment caused a time-dependent decline in cell viability, with a significantly stronger reduction in RIP1-deficient than in wild-type cells (Fig. 7e). Immunoblotting revealed a progressive loss of c-FLIP and faster accumulation of cleaved caspase-8 in RIP1-deficient cells. Moreover, densitometry analysis confirmed steeper slopes in RIP1-deficient cells, indicating accelerated c-FLIP depletion and earlier, stronger caspase-8 activation (Fig. 7f). Confocal imaging provided single-cell evidence, showing that caspase-8 signal changed from diffuse to punctate or oligomeric structures, with RIP1-deficient cells displaying denser and larger aggregates at later time points (Fig. 7g). Collectively, these data suggest that RIP1 supports c-FLIP expression, thereby restraining higher-order caspase-8 oligomerization and suppressing apoptosis in RIP3-deficient settings. In this context, the low stoichiometric occupancy of caspase-8 on RIP1 limits apoptotic amplification, while maintaining regulatory capacity over RIP3 activation. Therefore, RIP3 and caspase-8 form distinct supramolecular configurations within necrosomes, acting as opposing regulators that define the signaling bifurcation between apoptosis and necroptosis. Their stoichiometry and mutual inhibition constitute intrinsic checkpoints that govern cell fate decisions in response to TNF signaling.

Methods
Cell culture
HeLa, HEK293T, and mouse fibroblast L929 cells were obtained from the ATCC (CCL2, CRL-3216, CCL1). RIP1-KO, RIP3-KO and MLKL-KO HeLa or L929 cells were generated by using CRISPR/Cas9 method as previously reported13. The disruption of target gene was determined by the immunoblotting of cell lysates with antibodies. All cell lines were maintained at 37 °C and 5% CO2 in DMEM (Invitrogen) supplemented with 10% fetal bovine serum (Gibco), 2 mM L-glutamine, 100 U/mL penicillin, and 100 µg/mL streptomycin. Cells were routinely tested to be free of mycoplasma contamination.

Plasmids, reagents, and antibodies
RIP1, RIP3, MLKL, and MAP7 with Flag or HA tag were cloned into the lentiviral vector pBOB (Addgene, 12337). The primer sequences flanking the RIP1 region were agagaattcggatcccaaccagacatgtcc (BamH Ⅰ-RIP1) and cttccatggctcgaggttctggctgacgta (Xho Ⅰ-RIP1), the RIP3 region were agagaattcggatcctcgtgcgtcaagtta (BamH Ⅰ-RIP3) and cttccatggctcgagtttcccgctatgatt (Xho Ⅰ-RIP3), the MLKL region were agagaattcggatccatggaaaatttgaag (BamH Ⅰ-MLKL) and cttccatggctcgagcttagaaaaggtgga (Xho Ⅰ-MLKL), the MAP7 region were gaattcggatccatggtgcgaagcgaaaca (BamH Ⅰ-MAP7) and cttccatggctcgagagagccctcaggtgg (Xho Ⅰ-MAP7). Inducible expression of RIP3 was achieved by cloning the Flag-tagged RIP3 into the Tet-On expression vector (pLVX-TRE3G, Takara). Short hairpin RNA (shRNA) targeting MLKL was expressed from the lentiviral vector pLKO.1 (a gift from Dr. Qinxi Li at Xiamen University). All plasmids were verified by DNA sequencing. Recombinant human TNF-α (PHC3011) was from Thermo Fisher Scientific and used at 0.1–10 ng/mL. The following compounds were used: Smac mimetic SM-164 (APExBIO, A8815, 0.1 μM), pan-caspase inhibitor Z-VAD-FMK (Calbiochem, 627610, 20 μM), cycloheximide (MCE, HY-12320, 10 μg/mL) and doxycycline (Sigma, D9891, 1 μg/mL). The following antibodies were used throughout this report: anti-RIP1 (Cell Signaling, 3493, 1:150 for immunofluorescence (IF), 1:1,000 for western blotting (WB)), anti-phospho Ser166 RIP1 (Cell Signaling, 65746, 1:1,000 for WB), anti-RIP3 (Cell Signaling, 13526, 1:1,000 for WB), anti-phospho Ser227 RIP3 (Abcam, ab209384, 1:1,000 for WB), anti-MLKL (Abcam, ab184718, 1:200 for IF; 1:1,000 for WB), anti-phospho Ser358 MLKL (Abcam, ab187091, 1:1,000 for WB), anti-HA (Santa Cruz, sc-7392, 1:200 for IF, 1:1,000 for WB; Cell Signaling, 3724, 1:200 for IF, 1:1,000 for WB; ABclonal, AE008, 1:200 for IF), anti-Flag (Abmart, M20008L, 1:1000 for IF, 1:5,000 for WB; Biolegend, 637301, 1:200 for IF), anti-GAPDH (Proteintech, 60004-1-Ig, 1:5,000 for WB), goat anti-rabbit, anti-mouse and anti-rat secondary antibodies conjugated to Alexa Fluor 488, 568 or 647 (Thermo Fisher Scientific, A11034, A11004 or A21247, 1:1,000 for IF) and goat anti-rabbit, anti-mouse and anti-rat secondary antibodies conjugated to CF488A, CF568 or CF647 (Biotium, 20015, 20800, 20801, 20808 or 20809, 1:500 for IF). Detailed information for reagents and antibodies used in this study is provided in Supplementary Data 1 and 2, respectively.

Transfection and lentiviral infection
Cell transfection was performed using Turbofect reagent according to the manufacturer’s instructions (Thermo Fisher Scientific, R0531). For lentivirus production, HEK293T cells were transfected with lentiviral vectors and virus packaging plasmids by the calcium phosphate precipitation method. The virus-containing medium was collected 36–48 h later and added to HeLa cells with 10 μg/mL polybrene as indicated. The infectious medium was replaced with fresh medium 12 h later, and the cells were kept in culture until analysis. To reduce the MLKL expression, HeLa cells were stably infected with the lentiviral vector pLKO.1 containing MLKL-targeting shRNA.

Cell viability assay
Cell viability was assessed using a CCK-8 assay. Briefly, after treatments, cells were incubated with CCK-8 reagent (Apexbio, K1018) for 1 h at 37 °C, and the absorbance was measured at 450 nm. Viability was normalized to vehicle-treated controls. To specifically quantify necroptosis, cells were stained with Hoechst 33342 (1 μg/mL) and propidium iodide (PI, 5 μg/mL) for 20 min at 37 °C. Cells were immediately imaged using fluorescence microscopy. The total cell count was determined from Hoechst-positive nuclei, and necroptotic cells were defined as PI-positive. Image analysis was performed using Fiji/ImageJ software.

Immunoprecipitation and immunoblotting
Cell pellets were obtained in ice-cold PBS and re-suspended in lysis buffer (20 mM Tris-HCl, pH 7.5, 150 mM NaCl, 1 mM Na2EDTA, 1 mM EGTA, 1% Triton X-100, 2.5 mM sodium pyrophosphate, 1 mM β-glycerophosphate, 1 mM Na3VO4) plus protease inhibitor cocktail (MCE, HY-K0010). The re-suspended cell pellets were sonicated and centrifuged at 20,000 g for 30 min at 4 °C. The supernatant was immunoprecipitated with Flag-M2 affinity resin (Sigma, A2220) at 4 °C for 3 h or overnight. After the immunoprecipitation, the beads were washed four times in lysis buffer, and the immunoprecipitates were subsequently eluted with SDS sample buffer (50 mM Tris pH 6.8, 2% SDS and 10% glycerol). To analyze the oligomeric status of RIP1, RIP3, and MLKL, cells were directly treated with SDS sample buffer without β-mercaptoethanol and resolved by 4–12% gradient NuPAGE (Invitrogen, NP0336BOX). After electrophoresis, protein samples were transferred onto PVDF membranes. Membranes were blocked with 3% BSA for 1 h and incubated with primary antibodies overnight at 4 °C, followed by incubation with horseradish peroxidase-conjugated secondary antibodies. Proteins were visualized using the blot and gel imager (AI680, GE Healthcare). GAPDH was used as a loading control.

Confocal microscopy
For immunostaining, cells were grown on #1.5 coverslips (NEST, 801008) coated with poly-l-lysine (Sangon, E607015-0006). Cells were fixed with freshly prepared 3.7% formaldehyde for 15 min at room temperature and then permeabilized with 0.25% Triton X-100 in PBS. After blocking with 3% BSA for 1 h, samples were stained with primary antibodies overnight at 4 °C, washed three times with PBS, and incubated with secondary antibodies for 1 h at room temperature. Slides were mounted with antifade reagent (Invitrogen, P36934). For multi-channel imaging, extensive controls were performed to ensure that there was no non-specific staining or crosstalk between channels. These controls included (1) use of cells lacking one of the proteins of interest and/or (2) staining without one of the primary or secondary antibodies. All images were captured and processed with identical settings in the LSM980 laser scanning confocal microscope (ZEN 2019, Zeiss) with a 63×/1.40 numerical aperture (NA) oil objective. Duplicate cultures were examined and similar results were obtained in at least two independent experiments.

STORM imaging
STORM imaging was performed on an N-STORM microscope (Ti-E, Nikon Instruments). Briefly, the N-STORM system uses an Agilent MLC-400B laser launch with a red diode laser (647 nm, 300 mW; MPBC), a green solid-state laser (561 nm, 150 mW; Coherent), a blue solid-state laser (488 nm, 200 mW; Coherent), a violet diode laser (405 nm, 100 mW; Coherent) and a 100× NA 1.49 oil immersion objective. Emission fluorescence was separated with appropriate filters (FF02-520/28-25, FF01-586/20-25×3.5, and FF01-692/40-25; Semrock) and detected with a back-illuminated EMCCD camera (iXon DU897, Andor). Cells were pre-cultured on eight-well chamber coverslips (Thermo Fisher Scientific, 155409). Sample preparation for STORM imaging is similar to that for confocal microscopy. After labeling with appropriate fluorescence-conjugated antibodies, samples were immersed in GLOX imaging buffer containing 50 mM Tris (pH 8.0), 10 mM NaCl, 0.5 mg/mL glucose oxidase (Sigma, G2133), 40 μg/mL catalase (Sigma, C40), 10% (wt/vol) glucose, and 143 mM β-mercaptoethanol. For single-colour STORM imaging of CF 647, cells were exposed to a 647 nm laser at a power density of 2 kW cm-2. For two-colour STORM imaging, the CF 647 channel was acquired first, followed by the CF 568 channel at a laser power density of 1-3 kW cm-2. For three-colour STORM imaging, the CF 647 channel was acquired first, followed by the CF 568 channel, and finally the CF 488A channel at a laser power density of 1–3 kW cm-2. Quantitative STORM imaging was performed using a dye combination of AF647, CF568, and AF488. The imaging procedure was consistent across dyes, with the exception that AF488 was excited in a specialized DABCO imaging buffer containing 65 mM DABCO (BBI, A601472-0100), 30 mM DTT (BBI, B645939-001), 30 mM sodium sulfite (BBI, A100628-0500), prepared as reported previously50,51. For each channel, 10,000 to 20,000 frames were typically acquired at 70 fps. Final images were reconstructed using the SMAP platform following the provided guidelines.

STORM image analysis
For STORM image analysis, we used the SMAP software for data processing. As described previously52, the overall workflow includes steps such as Import, Fitting, Post-processing, and Rendering. To analyze molecular clusters, we employed the POCA software, which performs clustering based on Voronoi tessellation53. Localization data exported from SMAP was imported into POCA to extract parameters such as the area of each cluster and the total number of localizations. Since POCA does not provide direct measurements of cluster length, we developed a custom Python script to calculate the maximum Feret diameter using the localization and clustering information. For multi-color STORM datasets, channel separation was performed using frame-sequence metadata, followed by alignment and colocalization analysis using custom scripts and Microsoft Excel. Importantly, quantification of molecular stoichiometry ratios was based on localization counts rather than raw signal overlap, ensuring that the analysis reflects objective molecular metrics and reduces susceptibility to rendering or visualization artifacts.

Mass spectrometry analysis
Flag-RIP3- or Flag-MLKL-reconstituted L929 cells were treated with 10 ng/mL TNF for 0, 120, 210, 330, and 420 min for isolation of RIP3 or MLKL complexes. Ten 15-cm dish cells were collected for each time point experiment, and three biological replicates were carried out. After TNF treatment, cells were immediately washed twice with ice-cold PBS and harvested by scraping. The harvested cells were washed with PBS and lysed for 30 min on ice in HBS lysis buffer (12.5 mM HEPES, 150 mM NaCl, 1% Nonidet P-40, pH 7.5) with the protease inhibitor cocktail. Cell lysates were then spun down at 20,000 g for 30 min. The soluble fraction was collected and immunoprecipitated overnight with anti-Flag M2 antibody-conjugated agarose at 4 °C. Resins containing protein complexes were washed three times with HBS lysis buffer. Proteins were subsequently eluted twice with 0.2 mg/mL of 3× flag peptides in HBS lysis buffer for 30 min each time, and elution was pooled for a final volume of 300 μL. Proteins in the elution were precipitated with 20% trichloroacetic acid (TCA), and the pellet was washed two times with 1 mL cold acetone and dried in speedVac. TCA-precipitated proteins were re-suspended in 50 μL 1% SDC (sodium deoxycholate) in 10 mM TCEP (Tris(2-carboxyethyl)phosphine hydrochloride), 40 mM CAA (chloroacetamide), 100 mM Tris-HCl pH 8.5. 1% SDC was then diluted to 0.5% SDC with water, and trypsin (Sigma) was added at a ratio of 1:50 (trypsin:protein). The digestion was performed at 37 °C overnight. 1% TFA was added to stop the reactions, followed by centrifugation at 20,000 g for 10 min. The supernatants were subsequently transferred to C18 STAGEtips for desalting. Tryptic peptides were eluted with 70% acetonitrile/1% formic acid and dried in speedvac. Peptides were dissolved in 0.1% formic acid and analyzed by mass spectrometry in DDA and SWATH mode. MS analysis was performed on a TripleTOF 5600 (Sciex) mass spectrometry coupled to NanoLC Ultra 2D Plus (Eksigent) HPLC system. Peptides were first bound to a 5 mm × 500 μm trap column packed with Zorbax C18 5 μm 200 Å resin using 0.1% (v/v) formic acid/2% acetonitrile in H2O at 10 μL/min for 5 min, and then separated on a 75 μm × 15 cm in-house pulled emitter-integrated column packed with Magic C18 AQ 3 μm 200 Å resin. Predicted library generation and targeted analysis of DIA data were performed by DIA-NN (v1.8.1)54. “FASTA digest for library-free search” and “Deep learning-based spectra, RTs and IMs predication” were enabled. Output was filtered at 1% FDR to get significantly identified spectra and peptide lists.

Protein structure modeling
The published structures of RIP1–RIP3 hetero-amyloid core (Protein Data Bank (PDB) 5V7Z), RIP3 homo-amyloid core (PDB 7DAC), MLKL–RIP3 complex (PDB 7MON), Death effector domains (DED) complex of FADD&CASP8 (PDB 8YD8), TNF (PDB 1TNF), RIP1 domain (PDB 4NEU), RIP1 death domain (PDB 6AC5) and RIP3 kinase domain (PDB 4M66) were used in structural modelling and generating figures. The hetero-amyloid structure of RIP1–RIP3 in the published paper7 showed a slight twist with an angle of 5.4° ± 0.5° for RIP3 layers and 5.3° ± 0.3° for RIP1 layers, respectively. To generate the proposed RIP1–RIP3 model, the initial helical rise and twist angle of structure containing two layers of RIP3 and two layers of RIP1 (derived from PDB 5v7z) were set to 19.5 Å and 10.6°, respectively. The structures of death domains (DD) complex of FADD & RIP1, phosphorylated MLKL tetramer, and other full-length proteins and/or their complexes were generated by AlphaFold Server55. PyMOL (The PyMOL Molecular Graphics System, Version 3.0, Schrödinger, LLC.) was used to generate all the figures of structures.

Modeling principle
We established a quantitative dynamic model of TNF signaling-induced cell death complex assembly based on the law of mass action. The modeling process begins with identifying the fundamental biochemical reactions involved in the system, classifying them by reaction type, followed by translating each reaction into a rate law, and finally formulating a system of ordinary differential equations (ODEs) that capture the temporal evolution of all molecular species.

Reaction types and rate laws. Each reaction in the signaling cascade falls into one of the following fundamental categories:
• Association (binding): e.g., A + B → C Rate: r = k_on [A][B]
• Dissociation (unbinding): e.g., C → A + B Rate: r_assoc = k_off [C]
• Catalytic activation (e.g., phosphorylation, cleavage): e.g., A → A* Rate: r = k_cat [A]
• Degradation or inactivation: e.g., A* → deg+ Rate: r_deg = k_deg [A*]
• Multimerization or oligomer assembly (e.g., higher-order RIP3 complex): e.g., nA → A_n Rate: r_oligo = k_n [A]^n

Each reaction is assigned a rate constant k and contributes to the change in concentration of the involved species.

Construction of the ODE system
For each molecular species X_i, its concentration over time is described by:
d[X_i]/dt = Σ_{j=1}^f ν_{ij} * r_j(X, θ) (1)
Where:
• [X_i]: concentration of species i;
• r_j: rate of reaction j;
• ν_{ij}: stoichiometric coefficient of species X_i in reaction j;
• θ: vector of kinetic parameters.

This leads to a system of ODEs such as:
d[A]/dt = -k_on [A][B] + k_off [C]
d[B]/dt = -k_on [A][B] + k_off [C]
d[C]/dt = k_on [A][B] - k_off [C] - k_cat [C]
(2)
Each equation reflects the net effect of all reactions in which the species participates.

Basic components assembly modeling
To capture the hierarchical and sequential assembly of signaling complexes, we developed a mass-action-based kinetic model describing two fundamental interaction modules: a two-component kinase system (A-B) and a three-component extension (A-B-C). These modules abstract the core steps of scaffold recruitment, binding, and downstream activation.

1. Two-Component Assembly (A + B)
In the first stage, protein A binds to protein B, forming an intermediate complex A_m B_n, which then facilitates the activation of B. The assembly and activation steps are modeled as:
mA + nB ↔_{k1, k-1} A_m B_n →_{κ1} mA + nB*
• A_m B_n: the assembled but inactive complex formed by m units of A and n units of B.
• B*: the activated form of protein B.
• k_1, k_{-1}: binding and unbinding rates between A and B.
• κ_1: rate constant for irreversible activation of B.

2. Three-Component Assembly (A + B + C)
In the second stage, activated B (B*) recruits protein C and promotes its activation, forming a ternary complex:
nB* + hC ↔_{k2, k-2} B*_n C_h →_{κ2} nB* + hC*
• B*_n C_h: the intermediate complex consisting of n activated B units and h C units.
• C*: the activated form of protein C.
• k_2, k_{-2}: binding and unbinding rates between B* and C.
• κ_2: irreversible activation rate of C.

3. Model Variables and Interpretation
The stoichiometric coefficients m, n, h reflect the molecular configurations or oligomeric states of A, B, and C within their respective complexes. These variables allow the model to capture non-1:1 stoichiometries, which are common in supramolecular signaling assemblies. The complete set of ordinary differential equations derived from these reactions describes the time evolution of each species and complex. For example:
d[A_m B_n]/dt = k_1 [A]^m [B]^n - k_{-1} [A_m B_n] - κ_1 [A_m B_n] (3)
d[B*_n C_h]/dt = k_2 [B*]^n [C]^h - k_{-2} [B*_n C_h] - κ_2 [B*_n C_h] (4)
d[B*]/dt = κ_1 [A_m B_n] - k_2 [B*]^n [C]^h + k_{-2} [B*_n C_h] (5)
This modeling framework captures the essential sequential logic of multi-protein assembly: scaffold → recruitment → activation → downstream assembly. A detailed list of equations and parameter values is provided in Supplementary Table 1.

Determined necrosome assembly modeling
To systematically explore the emergent and intricate features of TNF-induced necrosome formation, we developed a mass-action-based kinetic model that incorporates the core molecular events of protein recruitment, binding, catalytic activation, and mutual inhibition. The model is formulated as a set of coupled ordinary differential equations (ODEs), following the principles described in the Modeling Principle section. It captures the time-dependent evolution of individual molecular species involved in necrosome assembly and downstream necroptosis signaling. The model includes the following key modules: 1). TNF-induced recruitment of RIP1 and TRADD to complex I; 2). Dissociation of RIP1 from complex I and its incorporation into the necrosome; 3). Assembly of necrosome via RIP1-RIP3 binding and phosphorylation; 4). Activation of MLKL by phosphorylated RIP3; 5). Crosstalk and mutual inhibition between RIP3 and caspase-8 (C8); 6). TRADD-mediated activation of C8. This section focuses on modeling the core necrosome assembly cascade, which includes the stepwise formation of RIP1-RIP3-MLKL complexes and their activation events.

Reactions and stoichiometry
Mass spectrometry-based quantitative analysis revealed the stoichiometric ratios of key components in the necrosome as RIP1:RIP3:MLKL = 1:3:2. The major reactions modeled are:
• Reaction R1: Binding of one active RIP1 molecule with three RIP3 molecules to form a ternary complex
RIP1_active + 3RIP3 ↔_{k1, k-1} RIP1_active RIP3_3
• Reaction R2: Catalytic phosphorylation of RIP3 by RIP1 within the RIP1-RIP3 complex
RIP1_active RIP3_3 →_{κ2} RIP1_active + 3RIP3_pho
• Reaction R3: Binding of three phosphorylated RIP3 molecules to two MLKL molecules
3RIP3_pho + 2MLKL ↔_{k3, k-3} RIP3_pho3 MLKL_2
• Reaction R4: Phosphorylation and activation of MLKL
RIP3_pho3 MLKL_2 →_{κ4} 3RIP3_pho + 2MLKL_pho

Where:
• RIP1_active: the activated form of RIP1 released from complex I
• RIP3, RIP3_pho: unphosphorylated and phosphorylated forms of RIP3
• MLKL, MLKL_pho: inactive and activated MLKL
• k, k_{-1}, κ_2, k_3, k_{-3}, κ_4: forward, reverse, and catalytic rate constants for each step

Differential equations
The dynamics of each species is governed by the following ODEs:
• For the RIP1-RIP3 ternary complex:
d[RIP1_active RIP3_3]/dt = k_1 [RIP1_active] [RIP3]^3 - k_{-1} [RIP1_active RIP3_3] - κ_2 [RIP1_active RIP3_3] (6)
• For free active RIP1:
d[RIP1_active]/dt = -k_1 [RIP1_active] [RIP3]^3 + k_{-1} [RIP1_active RIP3_3] + κ_2 [RIP1_active RIP3_3] (7)
• For phosphorylated RIP3:
d[RIP3_pho]/dt = 3κ_2 [RIP1_active RIP3_3] - 3k_3 [RIP3_pho]^3 [MLKL]^2 + 3k_{-3} [RIP3_pho3 MLKL_2] + 3κ_4 [RIP3_pho3 MLKL_2] (8)
• For the RIP3-MLKL complex:
d[RIP3_pho3 MLKL_2]/dt = k_3 [RIP3_pho]^3 [MLKL]^2 - k_{-3} [RIP3_pho3 MLKL_2] - κ_4 [RIP3_pho3 MLKL_2] (9)
• For activated MLKL:
d[MLKL_pho]/dt = 2κ_4 [RIP3_pho3 MLKL_2] (10)

The equations presented here focus on modeling the core necrosome assembly process and the key interactions between RIP1, RIP3, and MLKL. However, due to the multi-step, highly interconnected nature of necrosome formation, the actual system is more complex and involves additional molecular interactions. These include further crosstalk between RIP3, caspase-8, TRADD, and other components of the necrosome signaling network. As such, the full model comprises 23 reaction equations and 39 ODEs in total, capturing a broader range of protein-protein interactions, feedback loops, and regulatory mechanisms that govern necrosome assembly and its downstream effects. The complete set of equations and parameter values is provided in Supplementary Table 2.

Parameters values and initial amounts selection
Kinetic parameters. The selection of kinetic parameters for our model was based on experimental data, and these parameters were optimized using R-square fitting. The parameters include rate constants for various reactions such as binding/unbinding rates, catalytic activation rates, and stoichiometric coefficients. These values were chosen to be within biologically relevant ranges, with a focus on fitting the experimental data derived from time-course measurements of protein concentrations involved in necrosome assembly. The fitting procedure aimed to minimize the discrepancy between experimental and model-predicted values using the coefficient of determination (R-square), which quantifies the goodness of fit. The R-square value is calculated as:
R^2 = 1 - [Σ_{i=1}^N ([X_exp]_i(t_i) - [X_sim]_i(t_i; θ))^2] / [Σ_{i=1}^N ([X_exp]_i(t_i) - [X_exp]_i)^2] (11)
Where:
• [X_exp]_i(t_i) are the experimental concentration measurements at time t_i,
• [X_sim]_i(t_i; θ) are the model-predicted concentrations using the parameter set θ,
• [X_exp]_i is the mean value of the experimental concentrations,
• N is the number of data points used in the fitting.

The parameters were iteratively adjusted to maximize R^2, ensuring that the model predictions closely matched the experimental data, thereby reflecting the dynamic behavior of the signaling network.

Initial amounts of species. The initial concentrations of the species involved in necrosome assembly, such as RIP1, RIP3, and MLKL, were selected based on previously published studies and experimental proteomics data. These initial amounts were used to set the starting conditions for the ODE simulations. For species where direct initial concentration data were unavailable, reasonable estimates were made based on the literature, ensuring they remained within physiological ranges. The initial conditions were set for the concentrations of RIP1, RIP3, and MLKL at t=0, which were assumed to be in their inactive forms prior to TNF stimulation. These values were then used as the baseline for fitting the model.

Stochastic simulation of necrosome assembly dynamics
To account for intrinsic cell-to-cell variability, we performed large-scale stochastic simulations of TNF-induced necrosome assembly by systematically perturbing kinetic parameters and structural features within biologically plausible ranges.

Parameter space and Latin Hypercube sampling. We defined an p-dimensional parameter space θ={θ_1, θ_2, …, θ_p}, with each θ_i ∈ [θ_{i,min}, θ_{i,max}] sampled in log-scale according to prior physiological knowledge:
θ_i = logU(logθ_{i,min}, logθ_{i,max}) for i=1, 2, ..., n_p (12)
The sampling ranges were defined as:
• Kinetic constants (k): θ ∈ [0.1, 10]
• Catalytic or cooperativity terms (j): θ ∈ [10^-3, 10^2]
• Degradation rates (d): θ ∈ [10^-2, 1]
• RIP3 filament size (n): n ∈ {1, 2, …, 8} (discrete uniform)

Using Latin Hypercube Sampling (LHS), we generated N = 10,000 unique samples:
{θ^{(1)}, θ^{(2)}, …, θ^{(10000)}}
Each sample θ^{(i)} corresponds to a full set of reaction rates and initial conditions for a TNF-induced signaling network model.

Simulation of dynamics and efficiency computation
For each parameter set θ^{(i)}, the system of ODEs describing TNF-driven necrosome assembly was numerically integrated until a steady state or fixed time point t=T was reached. The core readout of the signaling response was the MLKL phosphorylation efficiency η^{(i)}, defined as:
η^{(i)} = [pMLKL]^{(i)}(T) / [MLKL_total]^{(i)} (13)
where pMLKL is the steady-state concentration of phosphorylated MLKL under parameter set i.

To explore how oligomeric configurations affect signaling, we introduced a variable n_RIP3 representing the stochastic RIP3 chain length. For each θ^{(i)}, η was evaluated at different n_RIP3 values:
η^{(i)}(n_RIP3) = f(θ^{(i)}, n_RIP3) (14)
This enabled generation of a phase map over the plane (n_RIP3, η), as illustrated in Fig. 6a.

Selection of high-efficiency models and statistical analysis. From the 10,000 simulated models, we extracted the top α=5% models that achieved the highest MLKL phosphorylation efficiency:
M_{top} = {θ^{(i)}: η^{(i)} ≥ η_{95%}} (15)
where η_{95%} is the 95th percentile threshold. For each model in M_{top}, the corresponding optimal RIP3 chain length n* was identified:
n* = argmax_{n_RIP3} η^{(i)}(n_RIP3) (16)
These values were then statistically analyzed to determine whether specific RIP3 configurations conferred robust and optimal signaling. Additionally, to explore the influence of TNF stimulus and expression levels of RIP3 or MLKL on necrosome topology, we repeated the above simulations under perturbed conditions (e.g., [TNF], [RIP3], [MLKL]) and analyzed shifts in the distribution of n*.

Computational implementation
All simulations were implemented in Python using the SciPy.integrate.odeint solver for ODEs. Sampling was performed using pyDOE’s Latin Hypercube Sampling routines. Each simulation ran independently in parallel. The entire codebase is available at: https://github.com/XMU-Xu/RIP3_assembly.git.

Statistics and reproducibility
For all datasets subjected to comparative analysis, the appropriate statistical method was selected based on the assessment of variance homogeneity as determined by GraphPad Prism. Depending on whether the standard deviation (SD) was equal, either parametric or non-parametric tests were applied accordingly within the software. All statistical tests were two-tailed. Where error bars are shown, they represent one standard deviation (±SD). p < 0.05 denotes statistical significance. All analyses were carried out using GraphPad Prism version 8.3.0 (538) (released October 15, 2019).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study identifies the RIP1-bound RIP3 homotrimer as the minimal functional unit of the necrosome, enabling signal amplification while preserving threshold sensitivity. A notable finding is that excessive RIP3 oligomerization attenuates necroptotic signaling, revealing a size-control mechanism dynamically regulated by RIP1 (restrictive) and RIP3 (expansive), with MLKL unexpectedly limiting assembly. In contrast, caspase-8 recruitment by RIP1 follows a linear, non-oligomeric pattern. These distinct assembly modes create a biphasic necroptotic response, illustrating how signalosomes balance configuration and stoichiometry for efficient signaling—providing new insights for therapeutic and synthetic biology applications. The super-resolution microscopy data offer valuable advances in understanding signalosome dynamics, though additional causal experiments would strengthen the conclusions, particularly regarding RIPK3 oligomer size control in signal amplification/limitation.\nSpecific Comments:\n1. MLKL’s inhibitory role:\nmost interestingly, the authors’ observation that p-MLKL negatively correlates with RIPK3 higher-order assembly aligns with prior structural work (PMID: 24095729) showing MLKL-bound RIP3 adopts an inactive conformation. This suggests a feedback mechanism where MLKL, as a substrate, restricts RIPK3 kinase activity by limiting RIPK3 fibrilization. To solidify this claim, the authors should provide direct experimental evidence (e.g., co-IP assays) demonstrating MLKL’s physical restriction of RIPK3 oligomerization, corroborating their correlation data (Figure 5).\n2. RIPK3 oligomerization and signaling:\nWhile the data show that RIPK3 expression positively correlates with fibril length (consistent with pro-necroptotic activity), the conclusion that \"excessive RIP3 homo-assembly attenuates signaling\" appears contradictory without MLKL context. To resolve this, I suggest the authors to perform time-course analyses tracking RIPK3 fibril length and cell death in MLKL- proficient vs. -deficient systems. A predicted outcome:\no Without MLKL: RIPK3 fibrils grow indefinitely without cell death.\no With MLKL: Fibril elongation halts (or reverses) as p-MLKL accumulates, linking fibril size control to signal transduction. This would unify the observations, emphasizing that RIPK3 oligomerization’s functional output depends on MLKL-mediated feedback.\n3. Minor correction:\no Figure 3G: \"oligermer\" → \"oligomer\"", "reviewer_2": "The manuscript by Li et al. reports the experimental characterization of necrosome assembly by using super-resolution microscopy (STORM) and mass spectrometry (MS). The necrosome is an important signaling complex in necrotic cell death that has broad implications in various human diseases. The results complement the existing structural data from NMR with in vivo STROM and MS data, thus providing important insights into the function and assembly of necrosome. The manuscript may benefit from addressing the following concerns before formal publication:\n(1) The abstract may need one or two sentences to describe the importance of necrosome assembly. The introduction may benefit from a brief introduction of the necrosome function and its implication in certain human diseases.\n(2) Can the authors discuss on the structural basis and rationality of the phase diagram of MLKL activation shown in Fig. 5a- c, perhaps based on the previously published NMR structure data or AlphaFold2/3-predicted models?\n(3) Can the authors discuss on the structural basis and rationality of their findings on the stoichiometry of necrosome assembly? How are their results reconciled with the previously published data?\n(4) There are numerous places that authors used exaggerating or hyperbolic words or phrases, which are unnecessary for scholarly presentation. For example, on page 10, the 2nd paragraph starts with “… overwhelmingly underscores …”. It is confusing why the authors used “overwhelmingly” here. On page 2, the last sentence reads “our findings challenge the conventional view of …”. But the results do not appear to really “challenge” but instead just “improve” the existent understanding of necrosome assembly and associated signal transduction. The use of “challenge” in several places is not well supported by the work itself, and make it read more like overselling the paper by the authors.\n(5) There are no sufficient details provided for their computational modeling. The Methods section may benefit from a revision that provides necessary details required for reproduction of their modeling results.", "reviewer_3": "This work by Li et al. provides an interesting angle for understanding how necrosome assembly works (e.g., its output as p- MLKL and the resulting cell death). This is a very unique way to analyze the necroptosis mechanism; it should definitely be published. However, some issues require clarification and reconsideration before publication. Moreover, since some of the technical details are not fully clear in the current version, I may not agree with all the analytical approaches the authors have used (but more clarification will help address my concerns; I look forward to a revised manuscript). Also, I am not an expert in mathematical modeling. Thus, all my inputs and concerns are from a cell biology and microscopy angle.\n1. Note that a majority of the audience for this manuscript are still cell biologists; thus, this version of the manuscript are still hard to us to digest. A more detailed navigation would be needed. And for the figure legend and figure description in the main text, it is too brief, lacking details about how the authors reach to these results. And sometimes, it is not clear whether the data are based on model calculations or experimental data. I am happy to read a more updated version with EXTENSIVE re-writing.\nSeveral examples,\nThe authors keep writing \"RIP3 trimer per RIP1\" etc., which really reflects the 3:1 ratio of RIP3, right? I think words like \"trimer\", \"dimer\" are very confusing, as in one necrosome, it contains more than 3 RIP3 molecules.\nFig. 3g how did cell death measured? How did RIP3 dimer control? Fig. 3h, how did p-RIP3 measured? by WB? any representative images? How was RIP1 level controlled? (just citing the previous publication was not sufficient for the audience to understand the experimental details, and don't put everything into the method, as it is hard to locate the useful information)\nFig. 4g, is this simulation from a model or real experimental data? how about 4h? if it is not experimental data, then experimental data verification is a must.\nFig. 5 g and i, how were the MLKL and RIPK3 levels determined? The author suggested by STORM, which means STORM the whole cells or just the necrosomes? What does \"amount of necrosome per cell\" mean? \"necrosome counts or intensity?\" etc.\nWithout sufficient technical details about how the analysis was conducted, my judgment on these data remains very preliminary.\n2. Based on the quantitative approach STORM microscopy, I would ask the authors to clarify\nWhy choose to measure the length of RIP1/3, MLKL etc? If they want to calculate the molecular ratio of RIP1/3 or MLKL, would the \"area\" make more sense? Further, should the authors use the \"fluorescent intensity\" of RIP1/3 MLKL in the necrosome to quantify the amounts of each molecule? The reason I am drawing this attention is that in the necrosome structures, for example in Fig. 5f, 4 h and 6h, although it seems that 6h has a longer RIP3 structure, but 4h structure is brighter. How can the authors suggest that there were more RIP3 molecules in 6h compared to 4h? If the authors use \"area\" or the \"total intensity\" to quantify RIP1/3 and MLKL in this work from the beginning to the end, what would the molecular ratio be? Still 1:3 (RIP1/3)? Based on the 4f data, alternative conclusion could be \"as more RIP3 was induced, necrosome can be more and RIP3 \"line structure\" can be longer, but it seems that RIP3 molecules in each necrosome is less as the lower intensity of RIP3 in each RIP3 \"line structure\" comprared to 8h to 4h or 6h to 4h.\" Why would this be impossible?\nLet's say the authors used anti-HA and anti-flag, each primary antibody binds to the tag (antigen) at a 1:1 ratio (one antibody to one antigen). However, the anti-HA is raised by rabbit and the anti-flag is raised by mouse. The anti-rabbit secondary antibody can bind to anti-HA with a 5:1 ratio (5 anti-rabbit bind to 1 anti-HA), and the anti-mouse binds to anti-flag with a 10:1 ratio (10 anti-mouse bind to 1 anti-flag). At the same time, there are 3 fluorophore molecules on each anti-rabbit, while there are 5 fluorophore molecules on each anti-mouse. Thus, even at the beginning, the HA antigen and flag antigen are in a 1:1 ratio. After the secondary antibody staining on STORM, the ratio would be 1X5X3 to 1X10X5, resulting in a 15:50 ratio. Thus, the authors need to clarify how they deal with this issue.\nThe most problematic figure is in Fig. 7. The lack of RIP1 would lead cells to more apoptosis is simply due to lack of Nf-kb activation and cFLIP and other anti-apoptosis molecular transcription, which has been known for years. If the authors want to deliver such information, then they should add CHX to block transcription, which will induce apoptosis when TNF is added. Then the authors can calculate different ratios of RIP1/3/casp8. The authors should also conduct the same experiments with the RIP1 inhibitor Nec-1 and utilize various RIP1 point mutations that can alter RIP1 conformation. Additionally, they should incorporate smac-mematic into the analysis. The authors must exclude the possibility that it is not RIP1/3 conformation, RIP1/3 kinase activity, or RIP1-mediated transcriptional regulation; then, the authors can conclude that \"configurations\" is the checkpoint of apoptosis/necroptosis.", "reviewer_4": "The authors utilized super-resolution microscopy to investigate the nanoscale organization of necrosomes. Specifically, they employed multi-color imaging to examine the stoichiometry of RIP1, RIP3, and MLKL within individual necrosomes and proposed that a homotrimeric RIP3 structure per RIP1 is the minimum functional subunit required to induce necroptosis. However, the quantitative analysis using STORM appears methodologically limited, and the results are not fully convincing due to several critical issues.\nThe necrosome puncta in Fig. 1c appear to be localized within the nucleus, which contradicts the current understanding and other figures in this manuscript that necrosomes form in the cytoplasm. The authors need to address this discrepancy. Additionally, the necrosome morphology in Figure 1c (e.g., puncta size and distribution) differs significantly from later figures (e.g., Figure 3d and Figure 4e). Were all images taken under identical laser illumination conditions? The fluorescence signal in the bottom row of Fig. 1c is significantly weaker than in the top row, suggesting possible inconsistencies in imaging parameters or sample preparation.\nThe necrosome patterns in Fig. 2b are quite different from the punctate structures in Fig. 1c. The authors should clarify whether this discrepancy reflects experimental variability, distinct necroptosis stages, or imaging artifacts.\nThe resolution of the STORM images in Fig. 2b is insufficient to determine whether rod-like necrosomes represent a common or rare structural state. The authors should quantify the proportion of punctate versus rod-like necrosomes during TSZ treatment to support their claims of morphological heterogeneity.\nThe proposed model in Fig. 2f is based on immunoprecipitation data reflecting average interactions across a population of cells. This bulk biochemical approach cannot resolve the stoichiometry or spatial organization of individual necrosomes, rendering the model speculative in the context of single-complex imaging.\nIn Figures 3d and 3e, the authors infer a RIP1:RIP3 ratio of 1:3 based on two-color STORM imaging, but this is problematic. First, RIP1 and RIP3 are expected to colocalize tightly within necrosomes (10-20 nm scale), yet Figures 3d and 3e show minimal overlap at the nanometer scale. Second, the dyes CF647 and CF568 have different photophysical properties (e.g., blinking kinetics, photon output), which may bias the localization patterns. Additionally, primary-secondary antibody labeling can introduce variability in epitope accessibility and signal amplification. The images in Figure 4e do not convincingly support the model shown in Figure 8.\nIn Fig. 8, the proposed RIP1-RIP3-MLKL assembly model lacks direct experimental validation from the imaging data. The absence of consistent nanoscale colocalization among RIP1, RIP3, and MLKL, combined with unresolved technical artifacts in multi-color STORM, critically undermines the credibility of the model."}, "author_response": "Point-by-point response to the reviewers’ comments\n“Decoding necrosome assembly: harmonizing signal amplification and attenuation\nthrough optimal RIP3 stoichiometry”\nWe sincerely thank all the reviewers for their thoughtful, constructive, and\nencouraging comments that have significantly helped us improve the clarity,\nrigor, and overall quality of our manuscript. In response, we have thoroughly\nrevised the manuscript and addressed each point raised. Below, we provide\ndetailed point-by-point responses. For clarity, the reviewers' comments are\npresented in italic and black, our responses are in blue, and changes made to\nthe manuscript are indicated in red.\nReviewer #1 (Remarks to the Author):\nThis study identifies the RIP1-bound RIP3 homotrimer as the minimal functional\nunit of the necrosome, enabling signal amplification while preserving threshold\nsensitivity. A notable finding is that excessive RIP3 oligomerization attenuates\nnecroptotic signaling, revealing a size-control mechanism dynamically\nregulated by RIP1 (restrictive) and RIP3 (expansive), with MLKL unexpectedly\nlimiting assembly. In contrast, caspase-8 recruitment by RIP1 follows a linear,\nnon-oligomeric pattern. These distinct assembly modes create a biphasic\nnecroptotic response, illustrating how signalosomes balance configuration and\nstoichiometry for efficient signaling—providing new insights for therapeutic and\nsynthetic biology applications. The super-resolution microscopy data offer\nvaluable advances in understanding signalosome dynamics, though additional\ncausal experiments would strengthen the conclusions, particularly regarding\nRIPK3 oligomer size control in signal amplification/limitation.\nResponse: We thank the reviewer for the careful reading and positive appraisal\nof our work, as well as for the constructive comments that have helped us\nfurther strengthen the manuscript.\nSpecific Comments:\n1. MLKL’s inhibitory role:\nmost interestingly, the authors’ observation that p-MLKL negatively correlates\nwith RIPK3 higher-order assembly aligns with prior structural work (PMID:\n24095729) showing MLKL-bound RIP3 adopts an inactive conformation. This\n1\nsuggests a feedback mechanism where MLKL, as a substrate, restricts RIPK3\nkinase activity by limiting RIPK3 fibrilization. To solidify this claim, the authors\nshould provide direct experimental evidence (e.g., co-IP assays) demonstrating\nMLKL’s physical restriction of RIPK3 oligomerization, corroborating their\ncorrelation data (Figure 5).\nResponse: We thank the reviewer for highlighting this important point. While\nmany studies have established MLKL as a downstream effector of RIP3 during\nnecroptosis, little attention has been paid to its potential feedback role in\nregulating upstream necrosome dynamics. Our original manuscript, through\nboth super-resolution imaging and mathematical modeling, suggested that\nMLKL not only receives signals from RIP3 but also negatively regulates RIP1–\nRIP3 supramolecular assembly (Figures 4 and 5).\nWe also appreciate the reviewer pointing us to relevant structural studies (Xie\net al., Cell Rep. 2013; Meng et al., Nat Commun. 2021), which show that MLKL\nbinding induces a conformational shift in RIP3 that could inhibit further\noligomerization. These findings support the hypothesis that MLKL imposes a\nnegative feedback mechanism by limiting RIP3 fibrilization.\nTo strengthen this conclusion, as the reviewer suggested, we have now\nperformed co-immunoprecipitation (co-IP) assays to directly test whether MLKL\nphysically restricts RIP3 oligomerization. As shown in Fig. R1, overexpression\nof MLKL significantly reduced the co-precipitation of HA-RIP3 with Flag-RIP3,\nindicating that MLKL interferes with RIP3–RIP3 interaction and thereby inhibits\nits oligomerization. This new result has been incorporated into the revised\nmanuscript (page 16, lines 22–25) as follows:\n“To confirm MLKL-mediated feedback on RIP3 activation, we examined the\ninteraction between Flag-RIP3 and HA-RIP3 in the presence or absence of\noverexpressed Myc-MLKL. MLKL expression reduced the amount of HA-RIP3\npulled down with Flag-RIP3, suggesting that MLKL weakens RIP3 homo-\ninteraction and thereby inhibits RIP3 oligomerization (Fig. 5j).”\noligomerization. j Co-immunoprecipitation in 293T cells transiently expressing\nHA-RIP3, Flag-RIP3, and Myc-MLKL. Lysates were immunoprecipitated with\nanti-Flag beads, and inputs/IPs were immunoblotted for Flag, HA, and MLKL.\nHA-RIP3 binding to Flag-RIP3 was quantified as IP/input ratio (mean ± SD,\nn=3). p value was calculated using an unpaired two-tailed Student’s t-test. ***p\n< 0.001.\n2. RIPK3 oligomerization and signaling:\nWhile the data show that RIPK3 expression positively correlates with fibril\nlength (consistent with pro-necroptotic activity), the conclusion that \"excessive\nRIP3 homo-assembly attenuates signaling\" appears contradictory without\nMLKL context. To resolve this, I suggest the authors to perform time-course\nanalyses tracking RIPK3 fibril length and cell death in MLKL-proficient vs. -\ndeficient systems. A predicted outcome:\no Without MLKL: RIPK3 fibrils grow indefinitely without cell death.\no With MLKL: Fibril elongation halts (or reverses) as p-MLKL accumulates,\nlinking fibril size control to signal transduction.\nThis would unify the observations, emphasizing that RIPK3 oligomerization’s\nfunctional output depends on MLKL-mediated feedback.\nResponse: We thank the reviewer for this excellent suggestion, which aligns\nclosely with one of core principles of our mathematical model—namely, that\nnecroptotic signaling is not indefinitely amplified, but rather balanced by\nfeedback regulation. The conclusion that “excessive RIP3 homo-assembly\nattenuates signaling” is indeed drawn in the presence of MLKL, and we agree\nthat this context should be explicitly emphasized.\nTo clarify this point and directly test the reviewer’s hypothesis, we performed\n3\nnew time-course experiments tracking RIP3 fibril growth and cell death in\nMLKL-proficient and MLKL-deficient HeLa cells stably expressing Flag-RIP3.\nAs shown in Fig. R2, in the absence of MLKL, RIP3 fibrils continue to elongate\nover time, but cell death remains minimal. In contrast, in MLKL-proficient cells,\nRIP3 fibril elongation plateaus as p-MLKL levels rise, coinciding with the onset\nof cell death.\nFig. R2 (Figure 5k-m in revised manuscript) MLKL restricts RIP3 fibril\nelongation. k-m Flag-RIP3–expressing wild-type or MLKL-KO HeLa cells\ntreated with TSZ over time. Cell death by PI uptake (mean ± SD, n = 3 biological\nreplicates) (k). Dual-color STORM of RIP3 and p-MLKL (l); insets show\nnecrosome morphology. RIP3 fibril lengths quantified (m; n = 67/111 structures\nat 2h, 64/135 at 4h for WT/KO). p values were calculated using Ordinary one-\nway ANOVA (k) and Kruskal-Wallis test (m). ***p < 0.001, ****p < 0.0001. Scale\nbars: 10 μm (overview) and 200 nm (insets).\nThese data support a negative feedback loop, in which MLKL activation inhibits\nfurther RIP3 supramolecular assembly, thereby converting an expanding signal\ninto a terminal output. The following paragraph has been added to the revised\nmanuscript (page 16, lines 25–29):\n“We further examined RIP3 fibril dynamics and necroptosis over time in MLKL-\n4\nproficient and MLKL-deficient cells (Fig. 5k–m). In MLKL-deficient cells, RIP3\nfibrils continued to elongate over time, but cell death remained low. In contrast,\nin MLKL-proficient cells, RIP3 fibril growth plateaued as p-MLKL levels\nincreased, followed by cell death. These data demonstrate that MLKL provides\nnegative feedback, constraining RIP3 fibril elongation and ensuring signal\ntermination.”\nWe are grateful to the reviewer for prompting these experiments, which allowed\nus to directly demonstrate the feedback inhibitory role of MLKL. This finding\nalso supports the broader view that necroptosis is a regulated, checkpoint-\ncontaining process, consistent with previous reports involving NF-κB, caspase-\n8, and ESCRT pathways. The MLKL–RIP3 feedback loop may represent an\nadditional layer of control ensuring appropriate necroptotic activation in diverse\ncellular contexts.\n3. Minor correction:\no Figure 3G: \"oligermer\" → \"oligomer\"\nResponse: We thank the reviewer for pointing out the spelling mistake. We\nhave corrected all typographical errors we could find throughout the manuscript.\nReviewer #2 (Remarks to the Author):\nThe manuscript by Li et al. reports the experimental characterization of\nnecrosome assembly by using super-resolution microscopy (STORM) and\nmass spectrometry (MS). The necrosome is an important signaling complex in\nnecrotic cell death that has broad implications in various human diseases. The\nresults complement the existing structural data from NMR with in vivo STROM\nand MS data, thus providing important insights into the function and assembly\nof necrosome. The manuscript may benefit from addressing the following\nconcerns before formal publication:\nResponse: We sincerely thank the reviewer for the thoughtful and encouraging\nassessment of our manuscript. We are grateful for the constructive suggestions,\nwhich have helped us improve both the clarity and rigor of our work. Below, we\nprovide detailed point-by-point responses to each of the reviewer’s comments.\n(1) The abstract may need one or two sentences to describe the importance of\n5\nnecrosome assembly. The introduction may benefit from a brief introduction of\nthe necrosome function and its implication in certain human diseases.\nResponse: Thank you for this insightful suggestion. We agree that a clearer\ncontextualization of necrosome function and its pathological relevance would\nbenefit the manuscript. Accordingly, we have revised both the abstract and the\nintroduction to explicitly emphasize the biological significance of necrosome\nassembly and its implications in human diseases.\nSpecifically, we have added the following sentence to the beginning of the\nabstract (page 1, lines 24–25):\n“Necrosome assembly is essential for necroptosis, a process implicated in\nneurodegeneration, ischemic injury, and inflammatory diseases.”\nIn the introduction (page 2, lines 22–28), we have added a new paragraph that\nreads:\n“Necrosome is a supramolecular signaling complex that serves as the central\nexecution module of necroptosis. Necrosome assembly not only ensures the\nelimination of infected or damaged cells but also promotes the release of\ndamage-associated molecular patterns (DAMPs), thus amplifying inflammatory\nresponses. Importantly, aberrant necrosome assembly or dysregulation of its\ncomponents has been implicated in a wide spectrum of pathological conditions,\nincluding ischemia-reperfusion injury, neurodegenerative diseases,\ninflammatory bowel disease, and certain types of cancer. As such, dissecting\nthe principles of necrosome assembly is critical for understanding the molecular\nlogic of necroptosis and for identifying therapeutic targets in inflammation-\nassociated diseases.”\n(2) Can the authors discuss on the structural basis and rationality of the\nphase diagram of MLKL activation shown in Fig. 5a-c, perhaps based on the\npreviously published NMR structure data or AlphaFold2/3-predicted models?\nResponse: We thank the reviewer for the insightful questions regarding the\nstructural basis and plausibility of the MLKL-activation phase diagram shown in\nFigure 5a–c. From a structural biology perspective, we integrated published\nhigh-resolution structural and functional data with our own imaging\nobservations to systematically analyze how the three key variables in Figure 5\nmodulate RIP3 polymerization.\nformation of RIP1-RIP3 hetero-amyloid fibers. Solid-state NMR has shown that\nthe RHIM segments of RIP1 and RIP3 alternately stack to form a highly stable\nβ-spine (Mompeán et al., Cell. 2018), in which core tetrads (e.g., IQIG/VQVG)\ntogether with Gln/Cys–Ser side-chain pairing stabilize the hetero-assembly\nthermodynamically (Fig. R3a). Therefore, elevated active RIP1 not only\nnucleates numerous short hetero-assemblies but can also geometrically “cap”\nelongating RIP3 homo-filaments, probably reducing their mean length (Fig.\nR3b).\nFig. R3 (Figure S7 in revised manuscript) Structural basis for the phase\ndiagram of necrosome assembly. a Structural models of RIP1–RIP3 hetero-\namyloid (PDB: 5V7Z) and RIP3–RIP3 homo-amyloid (PDB: 7DAC) fibrils. b, c\nProposed impact of varying TNF/RIP1 (b) and RIP3 (c) levels on necrosome\nconfiguration inferred from fibril stoichiometry. d Crystal structure of the MLKL-\nbound RIP3 complex (PDB: 7MON) showing RIP3 in an inactive conformation.\ne Schematic model of RIP3 distribution in the presence of high-level MLKL,\nsuggesting MLKL-mediated spatial restriction of RIP3 oligomerization.\nFor Figure 5b, higher RIP3 expression markedly promotes RIP3 homo-filament\nextension. This accords with the law of mass action: at essentially constant\nRIP1 levels, increasing the concentration of RIP3 monomers raises the\nprobability of RHIM axial pairing and β-sheet stacking, favoring longer polymers\n(Figure 3c). Prior work provided detailed cryo-EM/ssNMR structures of RIP3\ncapacity for stable homo-assembly.\nIn Figure 5c, we observe that higher MLKL expression is associated with a\npronounced shortening of RIP3 homo-filaments, indicating an inhibitory effect\nof MLKL on RIP3 polymerization. MLKL comprises an N-terminal four-helix\nbundle (4HB) effector domain and a C-terminal pseudokinase (psK) regulatory\ndomain. The MLKL psK binds RIP3 and is phosphorylated by it, which in turn\nreleases the 4HB to oligomerize and translocate to the plasma membrane.\nAlthough there is, to our knowledge, no atomic-resolution report of MLKL\ndirectly capping RIP3 homo-filament ends, previous studies have shown that\nMLKL binding induces a conformational change in RIP3 that renders it inactive\n(Fig. R3d), thereby attenuating its catalytic function (Xie et al., Cell Rep. 2013;\nMeng et al., Nat Commun. 2021). We therefore hypothesize that this\nconformational conversion might disrupt the axial stacking interfaces required\nfor continued RHIM-mediated extension, impeding further growth of RIP3\nhomo-filaments. Thus, under elevated MLKL expression, RIP3 assembly\nbecomes more dispersed into smaller aggregates rather than concentrating into\nlong fibrillar structures (Fig. R3e). This “distributed-assembly” tendency can be\nunderstood as RIP3 forming multiple smaller complexes under high-MLKL\nconditions to achieve efficient engagement with MLKL. While the detailed\nmechanism by which MLKL limits RIP3 polymerization remains to be fully\nelucidated, and consistent with the Reviewer 1’s comments, our additional data\nfurther substantiate the biochemical evidence that MLKL imposes a negative\nfeedback by limiting RIP3 polymerization (please see Figs. R1 and R2).\nFig. R1 (Figure 5j in revised manuscript) MLKL inhibits RIP3\noligomerization. j Co-immunoprecipitation in 293T cells transiently expressing\nHA-RIP3, Flag-RIP3, and Myc-MLKL. Lysates were immunoprecipitated with\n8\nanti-Flag beads, and inputs/IPs were immunoblotted for Flag, HA, and MLKL.\nHA-RIP3 binding to Flag-RIP3 was quantified as IP/input ratio (mean ± SD,\nn=3). p value was calculated using an unpaired two-tailed Student’s t-test. ***p\n< 0.001.\nFig. R2 (Figure 5k-m in revised manuscript) MLKL restricts RIP3 fibril\nelongation. k-m Flag-RIP3–expressing wild-type or MLKL-KO HeLa cells\ntreated with TSZ over time. Cell death by PI uptake (mean ± SD, n = 3 biological\nreplicates) (k). Dual-color STORM of RIP3 and p-MLKL (l); insets show\nnecrosome morphology. RIP3 fibril lengths quantified (m; n = 67/111 structures\nat 2h, 64/135 at 4h for WT/KO). p values were calculated using Ordinary one-\nway ANOVA (k) and Kruskal-Wallis test (m). ***p < 0.001, ****p < 0.0001. Scale\nbars: 10 μm (overview) and 200 nm (insets).\nIn response to the reviewer’s comment, we have added Figure S7 and the\nfollowing sentences in the revised manuscript (page 16, line 30–page 17, line\n5) as follows:\n“Our data and prior structures support a unified structural rationale for the phase\nbehavior in Fig. 5a–c (Fig. S7). TNF elevates RIPK1 recruitment, favoring the\nalternated RIPK1:RIPK3 RHIM β-spine and effectively capping or diverting\nRIP3 expression follows mass-action logic and promotes RHIM-mediated axial\nstacking, yielding longer homo-filaments. By contrast, higher MLKL expression\ncorrelates with shorter RIP3 filaments without a concomitant rise in MLKL\nphosphorylation (Fig. 5k–m). This is consistent with structural evidence that\nMLKL binding induces a conformationally inactive state of RIP3, reducing the\navailability of stacking-competent RHIM interfaces and redistributing RIP3 into\nsmaller assemblies. In summary, RIP3 supramolecular assembly is dynamically\nregulated by multiple factors (i.e., TNF dosage, RIP1 abundance, RIP3\nexpression, and MLKL levels), ensuring precise spatial control over necrosome\nformation and necroptotic output.”\n(3) Can the authors discuss on the structural basis and rationality of their\nfindings on the stoichiometry of necrosome assembly? How are their results\nreconciled with the previously published data?\nResponse: We thank the reviewer for raising this important point. For the\nproposed necrosome stoichiometry RIP1:RIP3:MLKL:CASP8 ≈ 2:6:4:1 (Fig.\nR4), we performed an integrative analysis combining structure-guided modeling,\nfunctional readouts, and prior literature, and we clarify below its structural\nrationality and consistency with published data.\nmolecular assembly. g Proposed model of the modular and cooperative\narchitecture of the necrosome. Published (PDB) and predicted (AlphaFold)\nstructural models illustrate domain-specific interactions: DD oligomerization\n(TNFR, TRADD, RIP1), RHIM-driven hetero-/homo-amyloid assembly (RIP1–\nRIP3), and downstream effector recruitment (FADD–caspase-8; RIP3–MLKL).\nStoichiometric ratios (RIP1:RIP3:MLKL:Caspase-8 ≈ 2:6:4:1) were derived\nfrom IP-MS analysis.\nFirst, the observation that RIP3 exceeds RIP1 is fully compatible with the known\nRHIM-mediated assemblies underlying necrosome formation. RIP1 and RIP3\nform hetero-amyloid fibers via alternating RHIM pairing (Fig. R4; PDB 5V7Z),\nwhereas RIP3 alone can self-assemble into homo-amyloid fibrils through the\nsame motif (Fig. R4; PDB 7DAC). Using the 5V7Z hetero-tetramer “single layer”\nas a seed and strictly applying the left-handed helical parameters derived from\nEM density map EMD-30622, we extended RIP3 homo-oligomeric units (7DAC)\nfrom both ends of the seed. These structure-guided extensions showed that the\ncomposite assembly becomes stable only when RIP3:RIP1 ≥ 6:2 (Fig. R5).\nfrom 2:3 to 2:6. RIP1 and RIP3 segments are shown in purple and green,\nrespectively; opaque models correspond to 250 ps simulation conformations,\nand the semi-transparent yellow model indicates the initial crystal-derived\nassembly. In the 2:3 model, edge-adjacent RIP3 loops are disordered and the\nupper β-strands deviate markedly from the starting structure. Incorporation of\nan additional RIP3 subunit (2:4) improves local loop organization but introduces\npronounced torsional distortion of the β-strands. The 2:5 assembly remains\npoorly ordered, whereas the 2:6 model exhibits enhanced structural regularity.\nSecond, the 2:6 relationship between RIP1 and RIP3 defines a minimal\nfunctional module that closes geometrically and functionally with four MLKL\nmolecules. In this 2:6 block, two RIP3 subunits primarily engage RIP1 to\nstabilize the hetero-seed, while the remaining four RIP3 subunits align to form\na RIP3 tetramer. Within this tetramer, each RIP3 kinase domain engages one\nMLKL pseudokinase domain (Fig. R4; PDB 7MON), so these four RIP3s recruit\nand phosphorylate four MLKL molecules. Activated MLKLs then assemble into\nthe tetrameric execution complex (Fig. R4, yellow assembly), a widely reported\nfunctional species. Consequently, the ratio RIP3:MLKL ≈ 6:4 is structurally and\nfunctionally well-matched.\nThird, regarding Caspase-8 (CASP8), the FADD–RIP1 DD interface and the\nFADD–Caspase-8 DED complex (Fig. R4; PDB 8YD8) constitute the proximal\nregulatory branch of the necrosome. Prior structural work indicates that in the\n12\npresence of cFLIP, Caspase-8 enters the complex via the tandem DED axis but\nfails to form an active catalytic dimer, remaining embedded as a non-catalytic\nmonomer (Fig. R6). Our revised manuscript also includes biochemical\nvalidation of this state (please see Fig. R13). Thus, one Caspase-8 monomer\nsuffices to capture its cFLIP-dependent gating role, supporting the 2:6:4:1\nstoichiometry.\nFig. R6 Cryo-EM structural analysis of FADD:Caspase-8 complexes\ndefines the catalytic dimer architecture required for co-ordinated control\nof cell fate. Adapted from Fox et al., Nat Commun. 2021.\nTaken together, the 2:6:4:1 ratio emerges as the minimal, stable functional unit\ndictated by four convergent constraints: (i) the 1:1 local geometry of RHIM-\nbased hetero-nucleation (RIP1:RIP3), (ii) the stability threshold of RIP3 homo-\nextension, (iii) saturable occupancy of a tetrameric MLKL execution unit, and\n(iv) cFLIP-regulated, limited incorporation of Caspase-8 as a proximal gate.\nThis stoichiometry is supported across geometric, kinetic, and functional\ndimensions by high-resolution structures and our data, providing a robust,\ntestable, and literature-consistent structural explanation for necrosome\nassembly.\nIn response to the reviewer’s comment, we have updated Figure 2g in the\nrevised manuscript and added the following paragraph (page 6, line 28–34)\nto elaborate:\n“This assembly ratio outlines an assembly unit that reflects a mechanistically\n13\noptimized stoichiometry of the necrosome: two RIP1 molecules together with\ntwo RIP3 molecules form a heterotypic RHIM ‘seed’ (PDB: 5V7Z); among six\nRIP3 molecules, four assemble into a RIP3 tetramer, the minimal execution unit\nof necroptosis (PDB: 7DAC), which activates MLKL (PDB: 7MON) and thereby\ndrives four MLKL monomers to form the functional MLKL tetramer. Caspase-8\nparticipates as a non-catalytic monomeric regulator (PDB: 8YD8). All of these\nfeatures are consistent with prior reports and represent a structurally and\nfunctionally coherent scheme for necrosome assembly.”\n(4) There are numerous places that authors used exaggerating or hyperbolic\nwords or phrases, which are unnecessary for scholarly presentation. For\nexample, on page 10, the 2nd paragraph starts with “… overwhelmingly\nunderscores …”. It is confusing why the authors used “overwhelmingly” here.\nOn page 2, the last sentence reads “our findings challenge the conventional\nview of …”. But the results do not appear to really “challenge” but instead just\n“improve” the existent understanding of necrosome assembly and associated\nsignal transduction. The use of “challenge” in several places is not well\nsupported by the work itself, and make it read more like overselling the paper\nby the authors.\nResponse: We sincerely appreciate the reviewer’s thoughtful feedback on the\ntone and language of our manuscript. Upon re-examination of the manuscript,\nwe acknowledge that certain phrases may indeed be interpreted as overly\nassertive or not sufficiently justified by the presented data. In response, we\nhave carefully revised the manuscript to ensure that all claims are appropriately\ntempered and strictly supported by experimental evidence.\nSpecifically:\nWe have replaced “overwhelmingly underscores” with a more neutral phrase\nsuch as “strongly supports” or “is consistent with.” We have substituted\n“challenge the conventional view” with “refine” or “extend current understanding\nof” necrosome assembly and signal transduction, which we believe better\nreflects the incremental and mechanistic nature of our findings.\n(5) There are no sufficient details provided for their computational modeling.\nThe Methods section may benefit from a revision that provides necessary\ndetails required for reproduction of their modeling results.\ntransparency and reproducibility in computational modeling. We fully agree that\ndetailed methodological information is essential for enabling validation and\nreuse of our modeling framework by other researchers. In response, we have\nsubstantially expanded the “Mathematical Modeling” subsection of the Methods\nsection to include the necessary technical details. Specifically, we now provide:\n“Modeling Principle\nWe established a quantitative dynamic model of TNF signaling-induced cell death\ncomplex assembly based on the law of mass action. The modeling process begins with\nidentifying the fundamental biochemical reactions involved in the system, classifying\nthem by reaction type, followed by translating each reaction into a rate law, and finally\nformulating a system of ordinary differential equations (ODEs) that captures the\ntemporal evolution of all molecular species.\n1. Reaction Types and Rate Laws\nEach reaction in the signaling cascade falls into one of the following fundamental\ncategories:\n• Association (binding): e.g., Rate:\nkon\nA+B → C rassoc = kon[A][B]\n• Dissociation (unbinding): e.g., Rate:\nkoff\nC → A+B rdissoc = koff[C]\n• Catalytic activation (e.g., phosphorylation, cleavage): e.g., Rate:\nkcat\n∗\nA → A rcat =\nkcat[A]\n• Degradation or inactivation: e.g., Rate:\nkdeg\n∗ ∗\nA → ∅ rdeg = kdeg[A ]\n• Multimerization or oligomer assembly (e.g., higher-order RIP3 complex): e.g.,\nRate:\nkn\nn\nnA → An roligo = kn[A]\nEach reaction is assigned a rate constant k and contributes to the change in concentration\nof the involved species.\n2. Construction of the ODE System\nFor each molecular species X, its concentration over time is described by:\ni\nR\nd[Xi]\n= νij⋅rj(X,θ)\ndt j=1\nWhere:\n15\n• [X]: concentration of species i;\ni\n• r: rate of reaction j;\nj\n• ν : stoichiometric coefficient of species X in reaction j;\nij i\n• θ: vector of kinetic parameters.\nThis leads to a system of ODEs such as:\nd[A]\n⎧ = −kon[A][B]+koff[C]\ndt\n⎪\n⎪d[B]\n= −kon[A][B]+koff[C]\ndt\n⎨\nd[C]\n⎪ = kon[A][B]−koff[C]−kcat[C]\n⎪ dt\nEach equation reflects th⎩e ⋯net effect of all reactions in which the species participates.\nBasic Components Assembly Modeling\nTo capture the hierarchical and sequential assembly of signaling complexes, we\ndeveloped a mass-action-based kinetic model describing two fundamental interaction\nmodules: a two-component system (A-B) and a three-component extension (A-B-C).\nThese modules abstract the core steps of scaffold recruitment, binding, and\ndownstream activation.\n1. Two-Component Assembly (A + B)\nIn the first stage, protein A binds to protein B, forming an intermediate complex A B ,\nm n\nwhich then facilitates the activation of B. The assembly and activation steps are\nmodeled as:\nk1 κ1\n∗\n• A\nm\nB\nn\n:the assembled but inmaActi+ven cBo\nk\nm⇌−1 plAemx Bfonrm→emd Aby+ mnB units of A and n units of B.\n• B : the activated form of protein B.\n∗\n• k ,k :binding and unbinding rates between A and B.\n1 −1\n• κ :rate constant for irreversible activation of B.\n1\n2. Three-Component Assembly (A + B + C)\nIn the second stage, activated B (B ) recruits protein C and promotes its activation,\nforming a ternary complex: ∗\nk2 κ2\n∗ ∗ ∗ ∗\nnB +hC k⇌−2 BnCh ⟶ nB +hC\n• B C : the intermediate complex consisting of n activated B units and h C units.\nn h\n∗\n• C : the activated form of protein C.\n∗\n16\n• k ,k : binding and unbinding rates between B and C.\n2 −2\n∗\n• κ : irreversible activation rate of C.\n2\n3. Model Variables and Interpretation\nThe stoichiometric coefficients m,n,h reflect the molecular configurations or oligomeric\nstates of A, B, and C within their respective complexes. These variables allow the\nmodel to capture non-1:1 stoichiometries, which are common in supramolecular\nsignaling assemblies. The complete set of ordinary differential equations derived from\nthese reactions describes the time evolution of each species and complex. For\nexample:\nd[AmBn] m n\n= k1[A] [B] −k−1[AmBn]−κ1[AmBn]\ndt\n∗\nd[BnCh] ∗ n h ∗ ∗\n= k2[B ] [C] −k−2[BnCh]−κ2[BnCh]\ndt\n∗\nd[B ] ∗ n h ∗\n= κ1[AmBn]−k2[B ] [C] +k−2[BnCh]\nThis modeling framewdotrk captures the essential sequential logic of multi-protein\nassembly: scaffold → recruitment → activation → downstream assembly. A\ndetailed list of equations and parameter values is provided in Supplementary Table 1.\nDetermined Necrosome Assembly Modeling\nTo systematically explore the emergent and intricate features of TNF-induced\nnecrosome formation, we developed a mass-action-based kinetic model that\nincorporates the core molecular events of protein recruitment, binding, catalytic\nactivation, and mutual inhibition. The model is formulated as a set of coupled ordinary\ndifferential equations (ODEs), following the principles described in the Modeling\nPrinciple section. It captures the time-dependent evolution of individual molecular\nspecies involved in necrosome assembly and downstream necroptosis signaling.\nThe model includes the following key modules:\n1). TNF-induced recruitment of RIP1 and TRADD to complex I;\n2). Dissociation of RIP1 from complex I and its incorporation into the necrosome;\n3). Assembly of necrosome via RIP1-RIP3 binding and phosphorylation;\n4). Activation of MLKL by phosphorylated RIP3;\n5). Crosstalk and mutual inhibition between RIP3 and caspase-8 (C8);\n6). TRADD-mediated activation of C8.\nThis section focuses on modeling the core necrosome assembly cascade, which\nincludes the stepwise formation of RIP1-RIP3-MLKL complexes and their activation\nevents.\n1. Reactions and Stoichiometry\nMass spectrometry-based quantitative analysis revealed the stoichiometric ratios of\n17\nkey components in the necrosome as RIP1:RIP3:MLKL = 1:3:2. The major reactions\nmodeled are:\n• Reaction R1: Binding of one active RIP1 molecule with three RIP3 molecules to form\na ternary complex\nk1\nRIP1active+3 RIP3 k⇌−1 RIP1active_RIP33\n• Reaction R2: Catalytic phosphorylation of RIP3 by RIP1 within the RIP1-RIP3\ncomplex\nκ2\nRIP1active_RIP33 →RIP1active+3 RIP3pho\n• Reaction R3: Binding of three phosphorylated RIP3 molecules to two MLKL\nmolecules\nk3\n• Reaction R4: Phosphorylation and activation of MLKL\nκ4\nRIP3pho3_MLKL2 →3 RIP3pho+2 MLKLpho\nWhere:\n• RIP1 : the activated form of RIP1 released from complex I\nactive\n• RIP3, RIP3 : unphosphorylated and phosphorylated forms of RIP3\npho\n• MLKL, MLKL : inactive and activated MLKL\npho\n• k , k , κ , k , k , κ : forward, reverse, and catalytic rate constants for each step\n1 -1 2 3 -3 4\n2. Differential Equations\nThe dynamics of each species is governed by the following ODEs:\n• For the RIP1-RIP3 ternary complex:\nd[RIP1active_RIP33]\ndt\n3\n• For free active= RkIP1[1R: IP1active][RIP3] −k−1[RIP1active_RIP33]−κ2[RIP1active_RIP33]\nd[RIP1active] 3\n= −k1[RIP1active][RIP3] +k−1[RIP1active_RIP33]+κ2[RIP1active_RIP33]\n• For pdhtosphorylated RIP3:\nd[RIP3pho] 3 2\n= 3κ2[RIP1active_RIP33]−3k3[RIP3pho] [MLKL] +3k−3[RIP3pho3_MLKL2]\ndt\n18\n+3κ4[RIP3pho3_MLKL2]\n• For the RIP3-MLKL complex:\nd[RIP3pho3_MLKL2]\ndt\n3 2\n• For activated =MLkK3[LR: IP3pho] [MLKL] −k−3[RIP3pho3_MLKL2]−κ4[RIP3pho3_MLKL2]\nd[MLKLpho]\n= 2κ4[RIP3pho3_MLKL2]\nThe equations presented hedret focus on modeling the core necrosome assembly\nprocess and the key interactions between RIP1, RIP3, and MLKL. However, due to the\nmulti-step, highly interconnected nature of necrosome formation, the actual system is\nmore complex and involves additional molecular interactions. These include further\ncrosstalk between RIP3, caspase-8, TRADD, and other components of the necrosome\nsignaling network. As such, the full model comprises 23 reaction equations and 39\nODEs in total, capturing a broader range of protein-protein interactions, feedback loops,\nand regulatory mechanisms that govern necrosome assembly and its downstream\neffects. The complete set of equations and parameter values is provided in\nSupplementary Table 2.\nParameters Values and Initial Amounts Selection\n1. Kinetic Parameters\nThe selection of kinetic parameters for our model was based on experimental data,\nand these parameters were optimized using R-square fitting. The parameters include\nrate constants for various reactions such as binding/unbinding rates, catalytic\nactivation rates, and stoichiometric coefficients. These values were chosen to be within\nbiologically relevant ranges, with a focus on fitting the experimental data derived from\ntime-course measurements of protein concentrations involved in necrosome assembly.\nThe fitting procedure aimed to minimize the discrepancy between experimental and\nmodel-predicted values using the coefficient of determination (R-square), which\nquantifies the goodness of fit. The R-square value is calculated as:\nN exp sim 2\n2 ∑i=1[Xi ](ti)−[Xi ](ti;θ)\nR = 1− 2\nN exp exp\n∑i=1[Xi ](ti)−[Xi ]\nWhere:\n• [Xexp](t) are the experimental concentration measurements at time t,\ni i i\n• [Xsim](t;θ) are the model-predicted concentrations using the parameter set θ,\ni i\n• is the mean value of the experimental concentrations,\nexp\n19\n[Xi ]\n• N is the number of data points used in the fitting.\nThe parameters were iteratively adjusted to maximize R2, ensuring that the model\npredictions closely matched the experimental data, thereby reflecting the dynamic\nbehavior of the signaling network.\n2. Initial Amounts of Species\nThe initial concentrations of the species involved in necrosome assembly, such as\nRIP1, RIP3, and MLKL, were selected based on previously published studies and\nexperimental proteomics data. These initial amounts were used to set the starting\nconditions for the ODE simulations. For species where direct initial concentration data\nwere unavailable, reasonable estimates were made based on the literature, ensuring\nthey remained within physiological ranges. The initial conditions were set for the\nconcentrations of RIP1, RIP3, and MLKL at t=0, which were assumed to be in their\ninactive forms prior to TNF stimulation. These values were then used as the baseline\nfor fitting the model.\nStochastic Simulation of Necrosome Assembly Dynamics\nTo account for intrinsic cell-to-cell variability, we performed large-scale stochastic\nsimulations of TNF-induced necrosome assembly by systematically perturbing kinetic\nparameters and structural features within biologically plausible ranges.\n1. Parameter Space and Latin Hypercube Sampling\nWe defined a np-dimensional parameter space Θ = {θ , θ , ..., θ }, with each θ ∈ [θ\nnp i\n, θ ] sampled in log-scale according to prior physiological knowledge:\ni,min i,max\n₁ ₂\nThe sampling rangθeis∼ wleorgeU d(elofingeθdi,m aisn:, logθi,max) for i = 1,2,…,np\n• Kinetic constants (k): θ ∈ [0.1, 10]\n• Catalytic or cooperativity terms (j): θ ∈ [10-3, 102]\n• Degradation rates (d): θ ∈ [10-2, 1]\n• RIP3 filament size (n): n ∈ {1, 2, ..., 8} (discrete uniform)\nUsing Latin Hypercube Sampling (LHS), we generated N = 10,000 unique samples:\n{Θ(1),Θ(2),...,Θ(10000)}\nEach sample Θ^(i) corresponds to a full set of reaction rates and initial conditions for\na TNF-induced signaling network model.\n2. Simulation of Dynamics and Efficiency Computation\nFor each parameter set Θ^(i), the system of ODEs describing TNF-driven necrosome\n20\nassembly was numerically integrated until a steady state or fixed time point t = T was\nreached. The core readout of the signaling response was the MLKL phosphorylation\nefficiency η^(i), defined as:\n(i)\n(i) [pMLKL] (T)\nη = (i)\nwhere pMLKL is the steady-state co[nMcLeKnLtrtaottiaol]n of phosphorylated MLKL under\nparameter set i.\nTo explore how oligomeric configurations affect signaling, we introduced a variable\nn representing the stochastic RIP3 chain length. For each Θ^(i), η was evaluated\nRIP3\nat different n values:\nRIP3\n(i) (i)\nThis enabled generation of a pηhas(ne RmIPa3p) =ovferΘ the,n pRlIaPn3e (n , η), as illustrated in Fig.\nRIP3\n6a.\n3. Selection of High-Efficiency Models and Statistical Analysis\nFrom the 10,000 simulated models, we extracted the top α = 5% models that achieved\nthe highest MLKL phosphorylation efficiency:\n(i) (i)\nwhere η is the 95th percentiMlet othpre=shΘold.: ηFor ≥eaηc9h5 %model in M , the corresponding\n95% top\noptimal RIP3 chain length n* was identified:\n∗ (i)\nn = argmax η (nRIP3)\nnRIP3\nThese values were then statistically analyzed to determine whether specific RIP3\nconfigurations conferred robust and optimal signaling. Additionally, to explore the\ninfluence of TNF stimulus and expression levels of RIP3 or MLKL on necrosome\ntopology, we repeated the above simulations under perturbed conditions (e.g., [TNF],\n[RIP3], [MLKL]) and analyzed shifts in the distribution of n*.\nComputational Implementation\nAll simulations were implemented in Python using the SciPy.integrate.odeint solver for\nODEs. Sampling was performed using pyDOE’s Latin Hypercube Sampling routines.\nEach simulation ran independently in parallel. The entire code-base is available at:\nhttps://github.com/XMU-Xu/RIP3_assembly.git.”\nWe hope these additions address the reviewer’s concerns and enhance the\nclarity, accessibility, and scientific value of our modeling efforts.\nReviewer #3 (Remarks to the Author):\nThis work by Li et al. provides an interesting angle for understanding how\nnecrosome assembly works (e.g., its output as p-MLKL and the resulting cell\ndeath). This is a very unique way to analyze the necroptosis mechanism; it\nshould definitely be published. However, some issues require clarification and\nreconsideration before publication. Moreover, since some of the technical\ndetails are not fully clear in the current version, I may not agree with all the\nanalytical approaches the authors have used (but more clarification will help\naddress my concerns; I look forward to a revised manuscript). Also, I am not an\nexpert in mathematical modeling. Thus, all my inputs and concerns are from a\ncell biology and microscopy angle.\nResponse: We sincerely thank the reviewer for their encouraging and\nconstructive comments. We are particularly grateful for the recognition of the\nunique perspective our study brings to understanding necrosome assembly and\nnecroptotic signaling. At the same time, we fully understand the concerns raised\nregarding clarity and technical detail in the original manuscript, and apologize\nfor any confusion caused.\nTo address these concerns of the reviewer, we have made significant revisions\nto the manuscript, including:\n-Implementing a more robust and reliable quantitative STORM analysis, aided\nby an internal MAP7 calibration reference (Fig. R10), and improving the clarity\nof methodological descriptions achieved through the new SMAP analysis\nplatform (Figs. R7, R8, R9 and R17);\n-Performing additional experimental validations to clarify key mechanistic\nfindings (Figs. 11–13);\n-Extensively revising figure legends, captions, and text to ensure transparency\nand readability, particularly from a microscopy and cell biology perspective.\nWe hope these revisions have resolved the reviewer’s concerns and have\nimproved the manuscript’s overall clarity, rigor, and accessibility. Below, we\nrespond to each comment in detail.\n1. Note that a majority of the audience for this manuscript are still cell biologists;\nthus, this version of the manuscript are still hard to us to digest. A more detailed\n22\nnavigation would be needed. And for the figure legend and figure description in\nthe main text, it is too brief, lacking details about how the authors reach to these\nresults. And sometimes, it is not clear whether the data are based on model\ncalculations or experimental data. I am happy to read a more updated version\nwith EXTENSIVE re-writing.\nResponse: We fully agree with the reviewer that the original version of the\nmanuscript lacked sufficient clarity and accessibility for a broader cell biology\naudience. We appreciate this important feedback and have made substantial\nrevisions to improve the manuscript’s readability and navigability. Briefly, we\nhave restructured the logical flow of the manuscript by reordering several\nfigures and data panels, particularly in revised Figures 2, 3, 4, 5, and 7 (A\ndetailed summary of these changes is provided at the end of this response). In\naddition, we have extensively rewritten the main text and greatly expanded\nfigure legends to provide more context. All revised sections have been\nhighlighted in yellow in the updated manuscript for ease of review. We\napologize for any confusion caused by the original presentation and hope that\nthese comprehensive revisions have significantly improved the manuscript’s\nclarity, logic, and accessibility for a cell biology readership.\nSeveral examples,\nThe authors keep writing \"RIP3 trimer per RIP1\" etc., which really reflects the\n3:1 ratio of RIP3, right? I think words like \"trimer\", \"dimer\" are very confusing,\nas in one necrosome, it contains more than 3 RIP3 molecules.\nResponse: We thank the reviewer for pointing out this important issue\nregarding terminology. We agree that the use of terms such as “trimer” or “dimer”\nin the original manuscript could be misleading, especially given that the\nnecrosome is known to be a high-molecular-weight (megaDalton-scale)\ncomplex containing multiple copies of RIP1, RIP3, and MLKL (Feoktistova et\nal., Mol Cell. 2011; Tenev et al., Mol Cell. 2011). Thus, these terms may\nincorrectly suggest fixed and discrete oligomeric states, which are not\nconsistent with the dynamic and variable nature of supramolecular assembly in\nnecroptosis.\nOur intention was not to imply fixed oligomers, but rather to describe average\nstoichiometric ratios derived from biochemical and imaging data. To avoid\nconfusion, we have revised the manuscript throughout by replacing phrases\n23\nsuch as “RIP3 trimer per RIP1” with more precise terminology, including “a 3:1\nstoichiometric ratio of RIP3 to RIP1” or “RIP3:RIP1 ratio of approximately 3:1.”\nWe hope these revisions will prevent misinterpretation and make the\npresentation clearer for both specialists and general cell biology readers.\nFig. 3g how did cell death measured? How did RIP3 dimer control? Fig. 3h,\nhow did p-RIP3 measured? by WB? any representative images? How was RIP1\nlevel controlled? (just citing the previous publication was not sufficient for the\naudience to understand the experimental details, and don't put everything into\nthe method, as it is hard to locate the useful information)\nResponse: We thank the reviewer for these helpful clarifications. We agree that\nour original description of Figures 3g and 3h lacked sufficient experimental\ndetail and may have been confusing, particularly in distinguishing between\nsimulation and experimental data. For the experimental validations (lower\npanels labeled “Exp” in Figures 3g and 3h), cell death was measured using\nPI/Hoechst staining followed by conventional imaging and counting of PI-\npositive nuclei; RIP3 dimer was achieved by 4-OHT-mediated RIP3-HBD\ndimerization; p-RIP3 levels were quantified by western blotting using a\nphospho-specific RIP3 antibody; RIP1 levels were controlled by shRNA-\nmediated knockdown. All relevant updates are now included in the Figure 3\nlegend and in the Results and Methods sections.\nPrompted by the reviewer’s earlier comment on “RIP3 trimer per RIP1”, we\nrecognized that the original lower panel of Figure 3g interrogated fixed RIP3\noligomeric states, which did not fully capture our emphasis on RIP1–RIP3\nstoichiometry. To address this, we replaced that dataset with newly acquired\nmulti-color quantitative STORM data in MLKL-KO cells, directly measuring\nRIP1:RIP3 ratios within individual necrosomes. These new results (Fig. R11)\nalign closely with the predictions of our mathematical model and strengthen the\nexperimental validation. Please see the dedicated responses to the 3# and 4#\ncomments of this reviewer for details (Figs. R10 and R11).\nnecrosomes in MLKL-deficient cells. l Three-color STORM of necrosomes\nin HA-RIP1/Flag-RIP3–expressing RIP1-MLKL-DKO HeLa cells treated with\nTSZ for 4 h, labeled for HA-RIP1 (green), Flag-RIP3 (purple), and p-RIP3\n(yellow). Insets highlight necrosome morphology with calculated Flag:HA ratios\nand p-RIP3 localizations. m Quantification of RIP3:RIP1 stoichiometry and\nrelative p-RIP3 levels (localizations) in individual necrosomes (n = 89). Scale\nbars: 10 µm (overview), 300 nm (insets).\nFig. 4g, is this simulation from a model or real experimental data? how about\n4h? if it is not experimental data, then experimental data verification is a must.\nResponse: We thank the reviewer for pointing out the importance of\ndistinguishing between experimental and modeling data. In the revised\nmanuscript, we have explicitly labeled Figures 4g and 4h as Simulation panels.\nSpecifically, Figure 4g shows predictions from a deterministic, mass-action\nODE model parameterized by fitting to our experimental measurements and\nevaluated across RIP1:RIP3 stoichiometric regimes. Figure 4h reports\nstochastic simulations that incorporate cell-to-cell heterogeneity under the\nsame stoichiometric settings. The stochastic statistics in Figure 4h converge\nwith the deterministic surfaces in Figure 4g and consistently identify a maximum\nin MLKL phosphorylation efficiency at a RIP1:RIP3 ratio of ~1:3.\nWe agree that experimental support is essential. While precisely constructing\npreset cellular necrosomes with varying RIP1:RIP3 ratios is technically\nchallenging, the reviewer’s follow-up suggestions (4# comment) of using\nmonoclonal antibodies to p-MLKL and p-RIP3 in the STORM quantification are\nhighly instructive. Following this guidance, we incorporated monoclonal\nantibodies against p-MLKL into a multi-color quantitative STORM workflow. This\nallowed us to link distributions of MLKL phosphorylation levels to the measured\n25\nlocal RIP1:RIP3 ratios. The resulting experimental trends mirror the model, with\nthe highest p-MLKL fraction at ~1:3 RIP1:RIP3 (Fig. R12). Please see the\ndedicated responses to the 3# and 4# comments of this reviewer for details\n(Figs. R10 and R12). Together, these additions clearly distinguish model from\nexperiment and provide experimental validation for the model-predicted\ndependence of MLKL phosphorylation on RIP1:RIP3 stoichiometry. All relevant\nupdates have been incorporated into the Figure 4 and the Results and Methods\nsections.\nFig. R12 (Figures 4i, j in revised manuscript). Quantitative STORM of\nnecrosomes in MLKL-proficient cells. i Three-color STORM of necrosomes\nin HA-RIP1/Flag-RIP3–expressing RIP1-KO HeLa cells treated with TSZ for 4\nh, labeled for HA-RIP1 (green), Flag-RIP3 (purple), and p-MLKL (yellow). Insets\nhighlight necrosome morphology with calculated Flag:HA ratios and p-MLKL\nlocalizations. j Quantification of RIP3:RIP1 stoichiometry and relative p-MLKL\nlevels (localizations) in individual necrosomes (n = 122). Scale bars: 10 µm\n(overview), 300 nm (insets).\nFig. 5 g and i, how were the MLKL and RIPK3 levels determined? The author\nsuggested by STORM, which means STORM the whole cells or just the\nnecrosomes? What does \"amount of necrosome per cell\" mean? \"necrosome\ncounts or intensity?\" etc.\nWithout sufficient technical details about how the analysis was conducted,\nmy judgment on these data remains very preliminary.\nResponse: We thank the reviewer for pointing out the need for clearer technical\ndescriptions of our analyses in Figures 5g and 5i. We apologize for any\nconfusion caused in the original version of the manuscript.\nTo clarify:\n26\n-The RIP3 and MLKL levels shown in Figures 5g and 5i were not determined\nby STORM imaging, but rather quantified by densitometric analysis of Western\nblot bands, as shown in Figures 5f and 5h, respectively. In the revision, we have\nre-analyzed these data to provide more precise quantification of relative protein\nabundance.\n-The term “amount of necrosome per cell” specifically refers to the number of\ndiscrete necrosome puncta per necroptotic cell, as visualized and counted via\nSTORM super-resolution microscopy. To avoid ambiguity, we have rephrased\nthis throughout the manuscript as “necrosome counts per cell.”\nWe agree that the original text lacked clarity in explaining how these\nquantifications were performed. In response, we have revised the legend of\nFigure 5 to state the methods used for protein quantification (western blot\ndensitometry) and necrosome quantification (STORM-based cluster counting).\nWe hope these clarifications and revisions have addressed the reviewer’s\nconcerns and improved the overall interpretability of the data.\n2. Based on the quantitative approach STORM microscopy, I would ask the\nauthors to clarify\nWhy choose to measure the length of RIP1/3, MLKL etc? If they want to\ncalculate the molecular ratio of RIP1/3 or MLKL, would the \"area\" make more\nsense? Further, should the authors use the \"fluorescent intensity\" of RIP1/3\nMLKL in the necrosome to quantify the amounts of each molecule? The reason\nI am drawing this attention is that in the necrosome structures, for example in\nFig. 5f, 4 h and 6h, although it seems that 6h has a longer RIP3 structure, but\n4h structure is brighter. How can the authors suggest that there were more RIP3\nmolecules in 6h compared to 4h? If the authors use \"area\" or the \"total intensity\"\nto quantify RIP1/3 and MLKL in this work from the beginning to the end, what\nwould the molecular ratio be? Still 1:3 (RIP1/3)? Based on the 4f data,\nalternative conclusion could be \"as more RIP3 was induced, necrosome can be\nmore and RIP3 \"line structure\" can be longer, but it seems that RIP3 molecules\nin each necrosome is less as the lower intensity of RIP3 in each RIP3 \"line\nstructure\" comprared to 8h to 4h or 6h to 4h.\" Why would this be impossible?\nResponse: We thank the reviewer for this insightful and technically important\ncomment. In the original manuscript, we used length measurements of RIP1,\nRIP3, and MLKL structures in STORM images as one of our primary readouts,\n27\nbased on the well-established finding that necrosomes form higher-order,\namyloid-like structures (Li et al., Cell. 2012; Liu et al., PNAS. 2017; Mompean\net al., Cell. 2018). Consistently, the rod-shaped morphology of necrosomes was\nclearly observed under STORM with lateral resolution ~20 nm. Thus, we\ninterpreted length as a proxy for necrosome assembly status.\nHowever, we fully acknowledge the reviewer’s concern that area-based\nmeasurements may offer a more general and reproducible quantification\nstrategy, especially for readers outside the necroptosis field. Indeed, area rather\nthan length is more commonly used for single-molecule localization microscopy\n(SMLM, including STORM in this study) quantification in many recent cell\nbiology studies (Wu et al., Trends Cell Biol., 2020). By contrast, fluorescence\nintensity is not a reliable metric in STORM, since signal is based on single-\nmolecule localizations rather than cumulative photon output (Baddeley &\nBewersdorf, Annu Rev Biochem. 2018).\nFollowing the reviewer’s suggestion, we decided to re-analyze our STORM\ndatasets using an area/localization-based approach in the revised manuscript.\nTo more precisely extract the valuable quantitative information from our STORM\nimages, we have adopted the SMAP platform (Ries, Nat Methods. 2020), which\nintegrates >200 modules for localization fitting, drift correction, postprocessing,\nand spatial clustering (Fig. R7). Localization data were subjected to Voronoi-\nbased clustering in POCA (Levet and Sibarita, Nat Methods. 2023), from which\nwe extracted cluster-level parameters, including area and localization counts.\nSince POCA does not natively measure length, we also implemented a custom\nPython script to calculate the maximum Feret diameter, allowing comparison\nbetween metrics.\nSTORM image analysis. This schematic outlines the analytical pipeline for\nprocessing and quantifying single-color STORM images. STORM datasets\nwere acquired as previously described, involving image acquisition, single-\nmolecule localization, drift correction, and coordinate transformation.\nLocalization data were subsequently processed using Voronoi-based clustering\nvia the POCA software to extract cluster-level metrics including area and total\nlocalization count. Cluster length was quantified by calculating the maximum\nFeret diameter from exported localization data using a custom Python script. All\nstatistical analyses were based on processed localization data. For further\ndetails, refer to the Methods section.\nAccordingly, we have described this analytical shift in the revised Methods\nsection (page 30, lines 34–39):\n“For STORM image analysis, we used the SMAP software for data processing.\nFitting, Post-processing, and Rendering. To analyze molecular clusters, we\nemployed the POCA software, which performs clustering based on Voronoi\ntessellation. Localization data exported from SMAP was imported into POCA to\nextract parameters such as the area of each cluster and the total number of\nlocalizations. Since POCA does not provide direct measurements of cluster\nlength, we developed a custom Python script to calculate the maximum Feret\ndiameter using the localization and clustering information.”\nUsing this updated workflow (Fig. R7), we re-analyzed the datasets in Figures\n2c, 5e, 5g, and 5i. As shown in Fig. R8 (revised Fig. 2c) and Fig. R9 (revised\nFigs. 5d–i), the key conclusions remain consistent with those obtained from\nlength measurements: RIP3 contributes more than RIP1 to the rod-shaped\nnecrosome assembly, where the stoichiometric relationship between RIP1 and\nRIP3 remains robust when quantified by area (revised Fig. 2c). More\nimportantly, TNF stimulation, RIP3 overexpression, and MLKL knockdown yield\nreproducible changes in necrosome morphology and abundance, mirroring the\ntrends we reported with length (revised Figs. 5d–i). Finally, to avoid any\nmisinterpretation based on apparent brightness differences, we standardized\nimage display parameters across all figures to eliminate potential brightness-\ndriven misinterpretations and have indicated updates in Figure 2 and relevant\nmain text (page 5, lines 1–7) as follows:\nFig. R8 (Figure 2c in revised manuscript) Area quantification of\nnecrosome imaged by STORM in necroptotic cells. c Area distributions of\nRIP1-, RIP3-, and MLKL-stained structures in HA-RIP3-expressing HeLa cells\ntreated with TSZ for 2 h (n = 53 RIP1, 114 RIP3, 63 MLKL) or 4 h (n = 77 RIP1,\n30\n“To analyze the rod-shaped structures of RIP1, RIP3 and MLKL, we quantified\nthe area of STORM-identified puncta (Fig. 2c). For RIP1 rods, the predominant\narea was ~17 × 10³ nm² at early stages (TSZ 2 h), with maximum values up to\n~39 × 10³ nm². At later time points (TSZ 4 h), the predominant area increased\nto ~20 × 10³ nm², with maxima up to ~51 × 10³ nm². RIP3 rods exhibited larger\nareas than RIP1, with a predominant value of ~42 × 10³ nm² and maxima of\n~103 × 10³ nm² at 4 h. MLKL rods showed areas similar to RIP1 rods during\nTSZ treatment. These findings indicate that RIP3 contributes more substantially\nthan RIP1 to rod-shaped necrosome assembly, consistent with previous reports\nshowing that additional RIP3 homopolymerization on the RIP1–RIP3 hetero-\namyloid platform is critical for necroptosis induction.”\nIn addition, we also indicated updates in Figure 5 and relevant main text (page\n16, lines 9–22) as follows:\nnecrosome imaged by STORM in cells under various conditions. d-i\nExperimental validation of TNF (d), RIP3 (f), and MLKL (h) effects on RIP3\nassembly via immunoblotting and STORM. Changes in RIP3 and MLKL\nphosphorylation, and corresponding rod-shaped RIP3 structures (STORM), are\nshown under varying TNF stimulation (d), doxycycline-induced RIP3\nexpression (f), and shRNA-mediated MLKL knockdown (h). Statistical analysis\nof areas and counts of rod-shaped RIP3 structures from STORM under different\nTNF doses (e), RIP3 expression levels (g), and MLKL expression levels (i).\nExperiments in (d-i) used Flag-RIP3-expressing HeLa cells treated with TSZ\n32\nunder graded TNF concentrations (d,e; n = 28,48,64,80 structures), induced\nRIP3 expression (f,g; n = 24,30,42,86), or MLKL knockdown (h,i; n =\n40,64,88,94). RIP3/MLKL levels were quantified by immunoblot densitometry.\nScale bars: 10 μm (overview), 200 nm (insets).\n“To test these model predictions, we performed STORM imaging analysis of\nRIP3 assembly in necrosomes under various TNF concentrations in Flag-RIP3\nHeLa cells. Immunoblotting confirmed that increasing TNF stimulation\nenhances RIP3 and MLKL phosphorylation (Fig. 5d, left panel). Consistently,\nlow TNF produced fewer but larger RIP3 assemblies, whereas high TNF yielded\nmore numerous but smaller assemblies, revealing a clear inverse relationship\nbetween TNF concentration and the degree of higher-order RIP3 assembly (Fig.\n5d, right panel and 5e). Thus, higher stimulation correlates with smaller RIP3\nassemblies but a larger count of necrosomes. We next employed doxycycline\n(Dox)-induced RIP3-WT expression to obtain varying RIP3 levels in cells (Fig.\n5f, left panel). Increased RIP3 expression leads to elevated RIP3 and MLKL\nphosphorylation, as well as the formation of larger RIP3 assemblies (Fig. 5f,\nright panel). Statistical analysis reveals a substantial rise in both RIP3 assembly\ndegree and the average count of necrosomes per cell with higher RIP3 levels\n(Fig. 5g). Thus, RIP3 abundance positively regulates both its own\nsupramolecular assembly and necrosome quantity.\nTo assess the role of MLKL, we knocked down MLKL to different expression\nlevels using MLKL-specific shRNA (Fig. 5h, left panel). While MLKL is not\nexpected to influence RIP3 assembly, imaging shows that lower MLKL levels\nresult in fewer but larger RIP3 assemblies (Fig. 5h, right panel and 5i),\nsuggesting MLKL not only receives signals from RIP3 but also negatively\nregulates RIP1-RIP3 supramolecular assembly.”\nversion, I suppose the authors used a secondary antibody. But did the authors\nknow how many CF647 molecules were on each secondary antibody? And\nwhether each primary antibody binds to how many secondary antibodies? Even\nif the primary antibody is monoclonal, it may still bind to multiple epitopes on\nthe antigen (although this is less likely, but it could be). Therefore, all of these\nparameters can compromise the quantification. Could you please clarify how\nthis part was calculated? Let me provide a hypothetical example to illustrate my\npoint.\nLet's say the authors used anti-HA and anti-flag, each primary antibody binds\nto the tag (antigen) at a 1:1 ratio (one antibody to one antigen). However, the\nanti-HA is raised by rabbit and the anti-flag is raised by mouse. The anti-rabbit\nsecondary antibody can bind to anti-HA with a 5:1 ratio (5 anti-rabbit bind to 1\nanti-HA), and the anti-mouse binds to anti-flag with a 10:1 ratio (10 anti-mouse\nbind to 1 anti-flag). At the same time, there are 3 fluorophore molecules on each\nanti-rabbit, while there are 5 fluorophore molecules on each anti-mouse. Thus,\neven at the beginning, the HA antigen and flag antigen are in a 1:1 ratio. After\nthe secondary antibody staining on STORM, the ratio would be 1X5X3 to\n1X10X5, resulting in a 15:50 ratio. Thus, the authors need to clarify how they\ndeal with this issue.\nResponse: We thank the reviewer for this important and technically challenging\nquestion. Indeed, quantifying the exact number of molecules by SMLM\n(STORM in this study) remains a major methodological hurdle. Although\nSTORM allows us to directly visualize the morphology and molecular assembly\nof necrosomes with exceptional spatial resolution (Figures 2–5), accurate\nabsolute quantification of protein copy number remains difficult due to technical\nuncertainty. These challenges have been discussed in depth in prior literature\n(e.g., Baddeley & Bewersdorf, Annu Rev Biochem. 2018). Even the most recent\nadvances in SMLM-based stoichiometry estimation (e.g., Masullo et al., Nat\nCommun. 2025) have only achieved experimental validation for small\ncomplexes (e.g., dimers or tetramers), not for supramolecular assemblies like\nnecrosomes.\nThe reviewer correctly notes that variations in antibody binding stoichiometry\nand fluorophore labeling can substantially distort the apparent molecular ratios.\nRecognizing this, in the original manuscript, we did not aim to determine\nabsolute counts, but rather designed our STORM experiments to ensure faithful\nrelative comparisons: In single-color STORM (original Figures 2 and 5), the\nsame protein was compared across conditions using identical antibodies,\nlabeling, and acquisition settings, minimizing bias. In dual-color STORM\n(original Figure 3), we used high-affinity monoclonal antibodies (anti-Flag, anti-\nHA) and commercial STORM-optimized secondary antibodies with ~1 CF647\nor CF568 dye per antibody. This antibody–dye combination is well\ncharacterized and has been successfully used in previous dual-color STORM\n34\nstudies (e.g., Chen et al., Nat Cell Biol., 2022). Thus, although we could not\nobtain absolute stoichiometry, the relative quantifications were internally\nconsistent and sufficiently robust to support the conclusions and mathematical\nmodeling.\nWe thank the reviewer for this stressful argument, which pushes our work move\nstep forward. To increase the quality and impact of our work, we decided to\nincorporate a challenge of our methodology to more directly and precisely\naddress the issue of molecular quantification. We used the microtubule-binding\nprotein MAP7 as an internal reference for quantitative STORM (Fig. R10g).\nMAP7 is well-suited for this purpose because microtubules are morphologically\nsimilar to necrosome rods (width: ~25 nm vs. ~40 nm by EM) and are easily\nrecognized in cells. We tagged MAP7 at both termini with Flag and HA (Flag-\nMAP7-HA), ensuring a defined 1:1 antigen ratio. In addition, we generated a\ndual Flag-tagged construct (2Flag-MAP7-HA, via a P2A system), yielding a\ntheoretical Flag:HA ratio of 2:1.\nTo maximize quantitation accuracy, we systematically screened all available\nantibodies in our laboratory and selected well-performing anti-Flag and anti-HA\nmonoclonal antibodies with high affinity and minimal nonspecific binding. We\nthen fixed, immunostained, and imaged cells expressing either Flag-MAP7-HA\nor 2Flag-MAP7-HA under identical conditions. Confocal microscopy revealed\nthe expected microtubule cytoskeleton in both cases, confirming labeling\nspecificity and robustness (Fig. R10h). Quantification of fluorescence intensity\nfurther demonstrated an appropriate ~2-fold increase (1.93 ×) in the Flag:HA\nsignal ratio in 2Flag-MAP7-HA cells compared to Flag-MAP7-HA (Fig. R10i).\nConsistently, STORM imaging of dual-tagged MAP7 showed that the average\nlocalization ratio of Flag to HA closely matched the predicted value (1.79 × vs.\n2.00 ×; Fig. R10j, k). Thus, although uncertainties in antibody recognition and\ndye photophysics remain—as the reviewer rightly pointed out—these new\nexperiments conceptually validate the quantitative potential of antibody-based\nSTORM when combined with high-quality reagents and appropriate calibration\ncontrols.\nmicrotubule-binding protein MAP7 as a calibration reference. g Design of\nthe MAP7-based calibration system. HeLa cells expressing Flag-MAP7-HA or\nFlag-MAP7-P2A-Flag-MAP7-HA (“2Flag-MAP7-HA”) are expected to display\nFlag:HA stoichiometries of 1:1 and 2:1, respectively. h-k Dual-color confocal (h)\nand STORM (j) images of HeLa cells expressing Flag-MAP7-HA or 2Flag-\nMAP7-HA, immunolabeled with anti-Flag (purple) and anti-HA (green)\nantibodies. Quantification of fluorescence intensity (i; n = 40 fields per group)\nand localization counts (k; n = 45 structures per group) confirmed Flag:HA ratios\nmatched expected values. Insets show dual-labeled tubulin with calculated\nFlag:HA ratios. Scale bars, 10 µm (overview) and 300 nm (insets).\nIn summary, while absolute stoichiometry remains technically challenging, our\n36\nnew calibration strategy with MAP7 provides direct validation that antibody-\nbased STORM can achieve quantitative accuracy under controlled conditions.\nWe emphasize this as proof-of-principle, and acknowledge in the Discussion\nthat further optimization will be necessary for absolute counting in complex\nassemblies. We have added these new results to Figure 3g–k and described\nthem explicitly in the manuscript (page 10, lines 5–13).\n“To experimentally validate the model predictions with reliable quantitative\nSTORM, we designed a microtubule-associated protein MAP7-based\ncalibration system. We introduced Flag-MAP7-HA and a dual-tagged 2×Flag-\nMAP7-HA construct (via a P2A system) into HeLa cells, ensuring defined\nantigen stoichiometry (Fig. 3g). Confocal microscopy confirmed robust labeling\nand canonical microtubule morphology in both constructs (Fig. 3h).\nQuantification of fluorescence intensity showed an expected ~2-fold increase\nin Flag:HA signal in 2Flag-MAP7-HA cells compared to Flag-MAP7-HA (1.93×;\nFig. 3i). Subsequent STORM imaging revealed that the average localization\nratio of Flag to HA closely matched the theoretical value (1.79× vs. 2.00×; Figs.\n3j and 3k). These results demonstrate that, despite inherent uncertainties in\nantibody recognition and dye photophysics, the MAP7-based reference system\nconceptually validates the quantitative potential of antibody-based dual-color\nSTORM when combined with high-quality reagents and calibration controls.”\nIn the Discussion (page 25, lines 19–23), the following sentences were added:\n“While absolute molecule counting by SMLM remains technically challenging,\nour new calibration strategy with MAP7 provides direct validation that antibody-\nbased STORM can achieve quantitative accuracy under controlled conditions.\nAs single-molecule imaging advances, it will elucidate the spatiotemporal\nprinciples of various signalosomes with absolute single-complex precision.”\npRIP3 into the STORM quantification. This will serve as the gold standard for\nexperimental calculations of the output, validating the mathematical model.\nResponse: We thank the reviewer for this valuable suggestion. We fully agree\nthat including phosphorylated RIP3 (p-RIP3) and phosphorylated MLKL (p-\nMLKL) in our STORM quantification would provide essential validation of the\nnecrosome’s signaling output. By combining this with the MAP7-based internal\n37\ncalibration strategy (Fig. R10), we were able to assess the activation status of\nRIP3 or MLKL within individual necrosomes of measurable RIP1:RIP3\nstoichiometry, thereby strengthening the experimental grounding of our\nmathematical model.\nFirst, we performed three-color STORM imaging in TSZ-treated RIP1-MLKL\ndouble-knockout (DKO) HeLa cells expressing Flag-RIP1 and HA-RIP3 (Fig.\nR11l). The immunostaining procedures, antibody selection (e.g., anti-Flag and\nanti-HA), and imaging/analysis workflows were identical to those used in MAP7\ncalibration, ensuring quantitative comparability. Using MAP7 as an internal\nreference, we determined the RIP3:RIP1 stoichiometry of individual\nnecrosomes at 4 h TSZ treatment (Fig. R11m), which ranged from ~1.0 to ~9.0,\nwith a mean of 3.6. To probe necrosome signaling output, we quantified RIP3\nactivation within each necrosome. The experimental distribution of p-RIP3\nclosely mirrors the model prediction (original Fig. 3e): p-RIP3 remains near\nbaseline when RIP3:RIP1 ≤2, exhibits a sharp, switch-like rise as the ratio\napproaches ~3, and then plateaus near maximal levels for ≥3. This\nconcordance demonstrates that the RIP3:RIP1 stoichiometric ratio functions as\na critical determinant, triggering pronounced signal amplification and switch-like\nbehavior in necrosome activation.\nFig. R11 (Figure 3l and 3m in revised manuscript) Quantitative STORM of\nnecrosomes in MLKL-deficient cells. l Three-color STORM of necrosomes\nin HA-RIP1/Flag-RIP3–expressing RIP1-MLKL-DKO HeLa cells treated with\nTSZ for 4 h, labeled for HA-RIP1 (green), Flag-RIP3 (purple), and p-RIP3\n(yellow). Insets highlight necrosome morphology with calculated Flag:HA ratios\nand p-RIP3 localizations. m Quantification of RIP3:RIP1 stoichiometry and\nrelative p-RIP3 levels (localizations) in individual necrosomes (n = 89). Scale\nbars: 10 µm (overview), 300 nm (insets).\nand HA-RIP3, using monoclonal p-MLKL antibodies (Fig. R12i). Quantification\nrevealed a unimodal distribution of p-MLKL, peaking at a RIP3:RIP1 ratio of ~3\n(Fig. R12j). This trend closely matched the model simulation, which predicts\nmaximal MLKL phosphorylation when each RIP1 incorporates ~3 RIP3\nmolecules. This concordance between experimental and simulated data\nsupports a model in which stoichiometric balance between RIP3 and RIP1 is\ncritical for efficient necrosome activation.\nFig. R12 (Figure 4i, j in revised manuscript) Quantitative STORM of\nnecrosomes in MLKL-proficient cells. I Three-color STORM of necrosomes\nin HA-RIP1/Flag-RIP3–expressing RIP1-KO HeLa cells treated with TSZ for 4\nh, labeled for HA-RIP1 (green), Flag-RIP3 (purple), and p-MLKL (yellow). Insets\nhighlight necrosome morphology with calculated Flag:HA ratios and p-MLKL\nlocalizations. j Quantification of RIP3:RIP1 stoichiometry and relative p-MLKL\nlevels (localizations) in individual necrosomes (n = 122). Scale bars: 10 µm\n(overview), 300 nm (insets).\nTaken together, we believe these new experiments address the reviewer’s\nconcerns and highlight the value of using internal counting references like\nMAP7 to strengthen quantitative STORM analyses. This strategy not only\nimproves the reliability of molecular stoichiometry assessments but also\nenables validation of key predictions from our mathematical simulations.\nImportantly, we now provide direct visualization of functional (phosphorylated)\nnecrosome components at single-complex resolution, thereby linking structural\norganization with signaling output. These new data have been incorporated into\nthe revised manuscript (Figures 3l, 3m, 4i, and 4j), with corresponding\nexpansions in the Results section (page 10, lines 14–23 and page 14, lines 16–\n21).\n“Using this system, we next examined RIP1 and RIP3 stoichiometry in\n39\nindividual necrosomes. Three-color STORM was performed in TSZ-treated\nRIP1/MLKL double-knockout (DKO) HeLa cells reconstituted with Flag-RIP1\nand HA-RIP3 (Fig. 3l). The immunostaining procedures, antibody selection (e.g.,\nanti-Flag and anti-HA), and imaging/analysis workflows were identical to those\nused in MAP7 calibration, ensuring quantitative comparability. Segmentation\nand overlap analysis showed a wide distribution of RIP3:RIP1 ratios across\nnecrosomes at 4 h post-TSZ, ranging from ~1.0 to ~9.0, with an average of 3.6\n(Fig. 3m). To probe necrosome signaling output, we quantified RIP3 activation\nwithin each necrosome. The experimental distribution of p-RIP3 closely mirrors\nthe model prediction (Fig. 3e): p-RIP3 remains near baseline when RIP3:RIP1\n≤2, exhibits a sharp, switch-like rise as the ratio approaches ~3, and then\nplateaus near maximal levels for ≥3. This concordance demonstrates that the\nRIP3:RIP1 stoichiometric ratio functions as a critical determinant, triggering\npronounced signal amplification and switch-like behavior in necrosome\nactivation.”\n“To experimentally validate the optimal RIP3 stoichiometry, we conducted three-\ncolor STORM imaging in RIP1-KO HeLa cells expressing Flag-RIP1 and HA-\nRIP3, using monoclonal p-MLKL antibodies (Fig. 4i). Quantification of p-MLKL\nas a function of the RIP3:RIP1 ratio exhibits a unimodal distribution of p-MLKL,\npeaking at a RIP3:RIP1 ratio of ~3 (Fig. 4j). This trend closely matched our\nsimulation (Figs. 4g and 4h), confirming that stoichiometric balance between\nRIP3 and RIP1 governs optimal necrosome activation. Together, these data\nuncover signal attenuation as a key regulatory mechanism that defines the\nspatial size and activity of RIP3 supramolecular assemblies in necroptosis.”\nthe RIP1/3 conformation change, transcriptional change, and other molecules,\nsuch as cFLIP.\nThe most problematic figure is in Fig. 7. The lack of RIP1 would lead cells to\nmore apoptosis is simply due to lack of Nf-kb activation and cFLIP and other\nanti-apoptosis molecular transcription, which has been known for years. If the\nauthors want to deliver such information, then they should add CHX to block\ntranscription, which will induce apoptosis when TNF is added. Then the authors\ncan calculate different ratios of RIP1/3/casp8. The authors should also conduct\nthe same experiments with the RIP1 inhibitor Nec-1 and utilize various RIP1\npoint mutations that can alter RIP1 conformation. Additionally, they should\n40\nincorporate smac-mematic into the analysis. The authors must exclude the\npossibility that it is not RIP1/3 conformation, RIP1/3 kinase activity, or RIP1-\nmediated transcriptional regulation; then, the authors can conclude that\n\"configurations\" is the checkpoint of apoptosis/necroptosis.\nResponse: We thank the reviewer for this insightful and critical comment, which\nhas prompted us to revisit both our experimental design and interpretation,\nparticularly regarding the role of c-FLIP and RIP1-mediated transcriptional\nregulation in shaping apoptotic outcomes.\nAs the reviewer correctly noted, c-FLIP is a key modulator of Caspase-8\nactivation, and its expression is regulated by NF-κB signaling downstream of\nRIP1. To directly test this point, we treated WT and RIP1-deficient HeLa cells\nwith TNF plus cycloheximide (TC) to block transcription and assessed\napoptosis. As expected, RIP1-deficient cells exhibited significantly more\napoptosis compared to WT cells (Fig. R13e). In parallel, Western blotting\nrevealed reduced c-FLIP expression in RIP1-deficient cells, especially under\nTC, correlating with enhanced caspase-8 cleavage (Fig. R13f), supporting the\nrole of RIP1 in promoting anti-apoptotic transcription. Confocal imaging further\nshowed more pronounced caspase-8 oligomerization in RIP1-deficient\napoptotic cells (Fig. R13g). These data suggest that RIP1 restrains apoptosis\nin part by sustaining c-FLIP expression, consistent with well-established\nliterature (Wang et al., Cell. 2008; Feoktistova et al., Mol Cell. 2011). Supporting\nstructural studies also show that in the presence of c-FLIP, caspase-8 is\nrecruited to the necrosome via tDED interactions but remains catalytically\ninactive as a monomer, thereby limiting apoptotic amplification (please see Fig.\nR6).\nactivation and apoptosis. e-g WT or RIP1-deficient HeLa cells treated with\nTNF plus cycloheximide (TC) for indicated durations. Cell viability was\nassessed using a CCK-8 assay (e; mean ± s.d.; n=3 biological replicates).\nWestern blot analysis was performed for the indicated proteins, with the levels\nof c-FLIP and cleaved caspase-8 quantified by relative band intensities.\nRepresentative of three independent experiments (f). Confocal images showing\ncaspase-8 distribution in TC-treated cells. Insets highlight enlarged caspase-8\nstructures (g). p values were calculated using Ordinary one-way ANOVA. *p <\n0.05, ***p < 0.001. Scale bars, 30 µm.\nAccordingly, we have updated our revised model (Figure 8) to explicitly include\nc-FLIP as a transcriptionally regulated checkpoint. We also added new\nexperimental data (revised Fig. 7e–g) to substantiate this revision. The main\ntext has been modified (page 22, lines 9–25) to reflect this addition:\n“Our quantitative assembly analysis shows that caspase-8 (C8) occupancy on\nRIP1 is low, and this stoichiometric arrangement is insufficient to drive robust\namplification of the apoptotic cascade in RIP3-deficient cells. Previous studies\nhave demonstrated that loss of RIP1 predisposes cells to apoptosis, mainly\nbecause of impaired NF-κB activation and reduced expression of anti-apoptotic\nproteins such as c-FLIP. To test whether c-FLIP functions as a gatekeeper of\nC8 activation and aggregation, we treated HeLa cells with TNF plus\n42\ncycloheximide (TC) to block transcription and performed time-resolved\ncomparisons between wild-type and RIP1-deficient cells at 0, 3, 6, and 9 h (Fig.\n7e–g). TC treatment caused a time-dependent decline in cell viability, with a\nsignificantly stronger reduction in RIP1-deficient than in wild-type cells (Fig. 7e).\nImmunoblotting revealed a progressive loss of c-FLIP and faster accumulation\nof cleaved C8 in RIP1-deficient cells. Moreover, densitometry analysis\nconfirmed steeper slopes in RIP1-deficient cells, indicating accelerated c-FLIP\ndepletion and earlier, stronger C8 activation (Fig. 7f). Confocal imaging\nprovided single-cell evidence, showing that C8 signal changed from diffuse to\npunctate or oligomeric structures, with RIP1-deficient cells displaying denser\nand larger aggregates at later time points (Fig. 7g). Collectively, these data\nsuggest that RIP1 supports c-FLIP expression, thereby restraining higher-order\ncaspase-8 oligomerization and suppressing apoptosis in RIP3-deficient\nsettings. In this context, the low stoichiometric occupancy of caspase-8 on RIP1\nlimits apoptotic amplification, while maintaining regulatory capacity over RIP3\nactivation. Therefore, RIP3 and caspase-8 form distinct supramolecular\nconfigurations within necrosomes, acting as opposing regulators that define the\nsignaling bifurcation between apoptosis and necroptosis. Their stoichiometry\nand mutual inhibition constitute intrinsic checkpoints that govern cell fate\ndecisions in response to TNF signaling.”\nMore broadly, we acknowledge that our current model is somehow simplified,\nfocusing primarily on the molecular stoichiometry of RIP1 and RIP3. In reality,\nadditional regulatory layers including protein conformational states, post-\ntranslational modifications can also be critical. We strongly agree on the value\nof the reviewer’s proposed experiments (Nec-1 inhibition, RIP1 point mutations,\nand SMAC mimetic). However, in our present system these studies would\nrequire new stable cell lines and systematic pharmacologic/mutational\nevaluations, which are beyond the scope and timeline of the current revision\nthat centers on stoichiometry. To ensure terminological precision and avoid\nimplying an overstated claim, we have tempered the language throughout and\nreplaced “configuration” with “stoichiometry” where appropriate. We now\nexplicitly discuss these limitations in the revised Discussion (page 25, lines 12–\n16):\n“Currently, our model is intentionally streamlined, focusing on stoichiometry as\na tractable entry point to decode necrosome assembly. Additional regulatory\nlayers including protein conformational changes, kinase activity, and post-\ntranslational modifications likely exert decisive control over complex assembly\nand signal propagation. Future studies should systematically incorporate these\nfactors, ideally via targeted perturbations (e.g., Nec-1, RIP1 mutants, SMAC\nmimetics) combined with quantitative single-cell imaging and modeling.”\nlack of details, I find it difficult to read and assess. Many of my concerns may\nhave already been addressed by the authors and some clarification will do the\njob. Thus, I am really looking forward to a detailed revision.\nResponse: The revisions now provide greater methodological detail, improved\nfigure clarity, and additional supporting experiments. We hope that these\ncomprehensive revisions have significantly improved the readability, rigor, and\noverall impact of the manuscript, and we greatly appreciate the reviewer’s\nconstructive feedback, which has been invaluable in refining our work.\nReviewer #3 (Remarks on code availability):\nI am not an expert in reviewing the code.\nReviewer #4 (Remarks to the Author):\nThe authors utilized super-resolution microscopy to investigate the nanoscale\norganization of necrosomes. Specifically, they employed multi-color imaging to\nexamine the stoichiometry of RIP1, RIP3, and MLKL within individual\nnecrosomes and proposed that a homotrimeric RIP3 structure per RIP1 is the\nminimum functional subunit required to induce necroptosis. However, the\nquantitative analysis using STORM appears methodologically limited, and the\nresults are not fully convincing due to several critical issues.\nResponse: We thank the reviewer for the careful evaluation and constructive\nfeedback. We acknowledge the limitations of the original STORM analysis and\nmodel interpretation, and in the revised manuscript we have improved the\nimaging quantification pipeline, incorporated calibration strategies, and added\nnew experimental validations. We believe these revisions substantially\nstrengthen the methodological rigor and support the robustness of our\nconclusions.\nwhich contradicts the current understanding and other figures in this manuscript\nthat necrosomes form in the cytoplasm. The authors need to address this\ndiscrepancy. Additionally, the necrosome morphology in Figure 1c (e.g., puncta\nsize and distribution) differs significantly from later figures (e.g., Figure 3d and\nFigure 4e). Were all images taken under identical laser illumination conditions?\nThe fluorescence signal in the bottom row of Fig. 1c is significantly weaker than\nin the top row, suggesting possible inconsistencies in imaging parameters or\nsample preparation.\nThe necrosome patterns in Fig. 2b are quite different from the punctate structures\nin Fig. 1c. The authors should clarify whether this discrepancy reflects\nexperimental variability, distinct necroptosis stages, or imaging artifacts.\nResponse: We thank the reviewer for their careful examination of Figure 1c\nand for pointing out the discrepancies in localization and morphology across\ndifferent figures. After re-evaluating the images in Figure 1c, we confirmed that\nthe puncta are indeed cytoplasmic and not nuclear. This is supported by\nHoechst staining, which delineates the nucleus and clearly shows that RIP1,\nRIP3, and MLKL colocalized puncta lie outside the nuclear boundary (Fig.\nR14c’). The apparent nuclear-like appearance and reduced signal intensity in\nthe lower row likely result from late-stage necroptotic cells that have undergone\nmembrane rupture, causing cytoplasmic proteins to diffuse into the nuclear\nregion and diminishing overall fluorescence due to content leakage.\nTo address this issue, we have replaced the affected panels with representative\nimages acquired from earlier-stage necroptotic cells, ensuring more consistent\ncytoplasmic localization and intensity (Fig. R14c). We also carefully reviewed\nand verified all other confocal images in the manuscript to ensure consistency\nin image quality and biological interpretation.\nRegarding the discrepancy in necrosome morphology between Figure 1c and\nFigures 3d and 4e, this arises from differences in imaging modalities. Figure 1c\npresents diffraction-limited confocal images ( 250 nm resolution), in which\nnecrosomes appear as puncta. In contrast, Figures 3d and 4e were acquired\n∼\nusing STORM super-resolution microscopy ( 20 nm resolution), which\nresolves individual necrosomes as elongated, rod-like structures at the\n∼\nnanoscale. This technical distinction accounts for the observed morphological\ndifferences raised by the reviewer. Collectively, these revisions strengthen the\n45\nclarity of Figure 1c and reinforce the conclusion that necrosomes form as\ncytosolic structures containing RIP1, RIP3, and MLKL.\nFig. R14 (Figure 1c in revised manuscript). Necrosomes form in the\ncytosol and contain RIP1, RIP3, and MLKL in necroptotic cells. c Confocal\nimages of MLKL-knockout (KO) HeLa cells reconstituted with HA-RIP3 and\nMLKL-Flag and stimulated with DMSO (control) or TSZ for 3 h. Fixed cells were\nimmunolabeled with antibodies against RIP1 (green), HA (magenta), and Flag\n(yellow). DNA was counterstained with Hoechst 33342 (blue). Enlarged regions\n(white boxes) depict cytosolic necrosomes (red arrows), demonstrating co-\nlocalization of all three proteins. Data are representative of two independent\nexperiments. Scale bars: 10 μm (overview); 2 μm (insets).\nThe resolution of the STORM images in Fig. 2b is insufficient to determine\nwhether rod-like necrosomes represent a common or rare structural state.\nThe authors should quantify the proportion of punctate versus rod-like necrosomes\nduring TSZ treatment to support their claims of morphological heterogeneity.\nResponse: We thank the reviewer for this important suggestion. To address it,\nwe quantitatively assessed the structural heterogeneity of RIP1–RIP3–MLKL\nassemblies during necroptosis. While confocal microscopy showed the\ndynamic co-localization of RIP1, RIP3, and MLKL (Figure 1), super-resolution\nSTORM imaging revealed both round and elongated rod-shaped assemblies\n(Figure 2b). To determine whether rod-shaped necrosomes represent a\nfrequent structural state or a rare phenomenon, we analyzed RIP1-, RIP3-, and\nshown in Fig. R15, the proportion of rod-shaped assemblies increases over\ntime, suggesting a progressive transition from early punctate oligomers to\nhigher-order rod-like supramolecular complexes. This dynamic shift\nunderscores that rod-shaped necrosomes are not rare artifacts but rather a\nprominent and functionally relevant configuration during the progression of\nnecroptosis.\nFig. R15 (Figure 2d in revised manuscript). Time-dependent transition\nfrom punctate to rod-shaped necrosomes during necroptosis. d\nProportions of round- versus rod-shaped RIP1, RIP3, and MLKL structures in\nFlag-RIP3–expressing HeLa cells treated with TSZ for 2 h or 4 h, classified\nbased on STORM images.\nAccordingly, we have included new quantification data in the revised manuscript\n(revised Figure 2d) and added the following statement in the Results (page 5,\nlines 7–9):\n“Notably, the temporal increase in the fraction of RIP1-, RIP3-, or MLKL-positive\nrod-shaped necrosomes reflects a structural maturation process, transitioning\nfrom early oligomeric puncta to stabilized, rod-like supramolecular complexes\nduring necroptosis (Fig. 2d).”\nThe proposed model in Fig. 2f is based on immunoprecipitation data reflecting\naverage interactions across a population of cells. This bulk biochemical approach\ncannot resolve the stoichiometry or spatial organization of individual necrosomes,\nrendering the model speculative in the context of single-complex imaging.\nResponse: We thank the reviewer for this insightful and important comment.\nresolve the precise stoichiometry or architecture of individual necrosomes. We\nappreciate the opportunity to clarify how these data were used in our study.\nFirst, although IP-MS lacks single-complex resolution, it remains a robust and\nwidely used method to probe the composition and stoichiometry of large protein\nassemblies. Several previous studies have successfully employed similar\nmethodologies to deduce the architecture of supramolecular assemblies and\ngenerate biologically meaningful insights (e.g., Shinohara et al., Science. 2014;\nKim et al., Nat Commun. 2024; Sica et al., Cell Death Differ. 2025).\nSecond, to build a predictive and testable model, initial boundary conditions\nmust be well defined. Without them, complex intracellular systems can become\nunderconstrained and yield ambiguous results. Co-IP and MS-derived\nstoichiometric ratios provided these input constraints, ensuring the model was\ngrounded in measurable biological data. Notably, our study already considers\nthe heterogeneity highlighted by the reviewer. We performed large-scale\nstochastic simulations by randomly sampling 104 distinct combinations of\nstoichiometries and kinetic parameters under various input conditions (original\nFigure 6). Strikingly, these simulations converged on robust dynamic behaviors\nthat mirrored the deterministic model, indicating that our conclusions are not\nartifacts of averaging but instead reflect conserved assembly principles.\nregulatory strategies of RIP3 assembly. a Schematic workflow outlining the\ninvestigation of regulatory strategies using randomized signaling models. A\nTNF signaling network model was constructed and subjected to LHS method\nto sample parameter variables within physiological ranges, including stochastic\ndegrees of RIP3 assembly. Kinetic analysis was performed on all sampled\nmodels, with the top 5% identified based on MLKL phosphorylation efficiencies.\nassembly under varying TNF, RIP3, and MLKL conditions. b Distribution of RIP3\nconfigurations and corresponding MLKL phosphorylation efficiencies in the top\n5% efficient models under different conditions. c Statistical results\ncorresponding to the distribution of the three scenarios shown in (b). d\nRegulation of TNF, RIP3, and MLKL on RIP3 configurations. e-f, Theoretical\npredictions and experimental validations of the impacts of TNF, RIP3, and MLKL\non MLKL/RIP3 phosphorylation efficiencies. g, Schematic illustrating strategic\nbalance between necrosome abundance and RIP3 configurations in cells: high\nnecrosome abundance favors shorter RIP3 assemblies, while low abundance\nnecessitates longer RIP3 assemblies.\nThird, we fully recognize the importance of characterizing individual\nnecrosomes at single-complex resolution. In the revised manuscript, we took a\nstep forward by enhancing our quantitative STORM analysis with an internal\ncalibration reference (MAP7, Fig. R10). This strategy enabled us to more\naccurately assess RIP1:RIP3 stoichiometries at the level of individual\nassemblies and correlate them with functional outputs such as p-RIP3 and p-\nMLKL accumulation (please see Figs. R11 and R12). The experimentally\nmeasured distributions of individual-necrosome stoichiometry and p-MLKL\nlevels (Fig. R12j) closely matched the distributions predicted by our stochastic\nmodel (Fig. 4h). While absolute molecular counting remains technically\nchallenging in complex cellular environments, our revised approach offers a\nmeaningful step toward resolving stoichiometry and function at the single-\ncomplex level.\nIn summary, our conclusions are not based solely on IP-MS but emerge from a\nmulti-layered strategy: IP-MS for ensemble stoichiometry, STORM imaging for\nspatial morphology and quantitative assessment, and mathematical modeling\nfor systems-level integration. Although each approach has limitations, their\nconvergence strengthens the overall robustness of our conclusions. We have\nadded the following text to the revised Discussion section (page 25, lines 17–\n23):\n“Besides, population-averaged biochemical approaches such as IP–MS cannot\nresolve individual necrosomes, but they provide essential constraints for\ntractable modeling. Importantly, large-scale stochastic simulations showed that\nnecrosome dynamics remained robust across diverse stoichiometries,\nsupporting the existence of conserved assembly principles. By integrating\nensemble-level IP-MS, in situ quantitative STORM imaging, and computational\nmodeling, we present a cross-scale framework that reveals key features of\nnecrosome organization and signaling. As single-molecule imaging methods\ncontinue to advance, future studies will refine these principles with absolute\nsingle-complex resolution.”\nIn Figures 3d and 3e, the authors infer a RIP1:RIP3 ratio of 1:3 based on two-\ncolor STORM imaging, but this is problematic. First, RIP1 and RIP3 are\nexpected to colocalize tightly within necrosomes (10-20 nm scale), yet Figures\n3d and 3e show minimal overlap at the nanometer scale.\nResponse: We thank the reviewer for this important observation. We agree that\nRIP1 and RIP3 are expected to interact closely within necrosomes. However,\nthe extent of their spatial overlap at nanometer resolution has not been well\ndefined in prior imaging studies. While conventional confocal microscopy\nreveals substantial colocalization of RIP1 and RIP3 as diffraction-limited puncta\n(Figure 1c), STORM imaging provides a lateral resolution of ~20 nm, enabling\nus to resolve the spatial organization of individual protein molecules within\nnecrosomes.\nThe apparent \"minimal overlap\" between RIP1 and RIP3 observed in Figures\n3d and 3e likely reflects both the intrinsic molecular heterogeneity of necrosome\ncomposition and the high sensitivity of STORM to variations in local protein\nabundance. As shown in many necrosomes of Fig. R16a (original Figure 3d),\nwe observe clear spatial overlap or close apposition of RIP1 and RIP3\nmolecules. In cases with apparently reduced overlap, the effect can be\nattributed to weaker local signals or image-rendering thresholds. Indeed, when\nthe visualization parameters are adjusted to display lower-intensity signals,\neven those structures with seemingly \"minimal overlap\" show spatial continuity\nbetween RIP1 and RIP3 (Fig. R16b). This suggests that, despite appearance,\nRIP1 and RIP3 remain in close proximity at the nanoscale.\nRepresentative dual-color STORM images of RIP1–RIP3 assemblies in\nnecroptotic HA-RIP3 and MLKL-Flag-reconstituted MLKL-KO HeLa cells\ntreated with TSZ. Images were re-rendered with saturation-enhanced display\nsettings to visualize low-intensity signals. Asterisks mark weak localization\nregions contributing to apparent minimal overlap. Scale bar, 100 nm.\nTo improve rigor and reduce reliance on visual impression, we updated our\nanalysis pipeline. Using SMAP for multi-color STORM data, we implemented\nlocalization-based metrics (area, localizations) combined with POCA clustering\nand strict image registration for three-color datasets (Figs. R7 and R17).\nSupported by our MAP7 calibration reference (Fig. R10, see next response for\ndetails), this approach allows more quantitative evaluation of RIP1:RIP3\nstoichiometry independent of potential rendering artifacts. Consequently, we\nreplaced the descriptive original panels in Figures 3d and 3e with revised\nquantitative results that directly verify our modeling predictions (Fig. R11,\nsee next response for details). Please note, our quantification of RIP1:RIP3\nratios is not based solely on signal overlap but on objective metrics derived from\nlocalization counts, which are less susceptible to rendering artifacts.\nSTORM image analysis. Clusters in individual channels (RIP1: red; RIP3:\ngreen; p-RIP3: yellow) were segmented using POCA, followed by intra- and\ninter-channel overlap analysis. For each overlapping cluster, area and\nlocalization counts were quantified, enabling calculation of RIP1:RIP3\nstoichiometry and relative p-RIP3 levels within individual necrosomes. Detailed\nmethods are described in the Methods section.\nIn summary, we have clarified these points in the Methods section (page 30,\nlines 39–43) and emphasized that our quantitative stoichiometric analyses rely\non localization counts rather than simple overlap, minimizing potential\nvisualization artifacts. We are grateful to the reviewer for highlighting this issue,\nwhich prompted us to refine both the imaging analysis and presentation for\nimproved clarity and rigor.\n“For multi-color STORM datasets, channel separation was performed using\nframe-sequence metadata, followed by alignment and colocalization analysis\nusing custom scripts and Microsoft Excel. Importantly, quantification of\nmolecular stoichiometry ratios was based on localization counts rather than raw\n53\nsignal overlap, ensuring that the analysis reflects objective molecular metrics\nand reduces susceptibility to rendering or visualization artifacts.”\nSecond, the dyes CF647 and CF568 have different photophysical properties\n(e.g., blinking kinetics, photon output), which may bias the localization patterns.\nAdditionally, primary-secondary antibody labeling can introduce variability in\nepitope accessibility and signal amplification. The images in Figure 4e do not\nconvincingly support the model shown in Figure 8.\nResponse: We thank the reviewer for highlighting these important points,\nwhich also echo similar concerns raised by Reviewer 3 (comment #3). We fully\nagree that differences in fluorophore photophysics (e.g., blinking kinetics,\nphoton yield) as well as uncertainties inherent to primary–secondary antibody\nlabeling (such as epitope accessibility and potential signal amplification) can\nbias localization patterns and complicate stoichiometric interpretation in\nSTORM. To mitigate these challenges, we carefully designed our experiments\nto ensure that all comparisons were as faithful as possible. In single-color\nSTORM experiments (original Figures 2 and 5), we analyzed the same protein\nacross different perturbations using identical antibodies, labeling protocols, and\nimaging settings, thereby minimizing artifacts from dye or antibody variability.\nIn dual-color STORM experiments (original Figure 3), we selected high-affinity\nmonoclonal antibodies (anti-Flag and anti-HA) in combination with commercial\nSTORM-optimized secondary antibodies conjugated to CF647 or CF568, dyes\nthat are supplied at one fluorophore per antibody on average and have been\nwidely validated in previous quantitative SMLM studies (e.g., Chen et al., Nat\nCell Biol. 2022). While our original goal was not to achieve absolute molecule\ncounting, these methodological controls allowed us to extract reproducible\nrelative stoichiometric trends in necrosome assembly. Importantly, these\nSTORM-derived results were further cross-validated by biochemical\nmeasurements and mathematical modeling, which together provide convergent\nsupport for our conclusions.\nWe thank the reviewer for this stressful argument, which pushes our work move\nstep forward. To increase the quality and impact of our work, we decided to\nincorporate a challenge of our methodology to more directly and precisely\naddress the issue of molecular quantification. We used the microtubule-binding\nprotein MAP7 as an internal reference for quantitative STORM (Fig. R10g).\nsimilar to necrosome rods (width: ~25 nm vs. ~40 nm by EM) and are easily\nrecognized in cells. We tagged MAP7 at both termini with Flag and HA (Flag-\nMAP7-HA), ensuring a defined 1:1 antigen ratio. In addition, we generated a\ndual Flag-tagged construct (2Flag-MAP7-HA, via a P2A system), yielding a\ntheoretical Flag:HA ratio of 2:1.\nTo maximize quantitation accuracy, we systematically screened all available\nantibodies in our laboratory and selected well-performing anti-Flag and anti-HA\nmonoclonal antibodies with high affinity and minimal nonspecific binding. We\nthen fixed, immunostained, and imaged cells expressing either Flag-MAP7-HA\nor 2Flag-MAP7-HA under identical conditions. Confocal microscopy revealed\nthe expected microtubule cytoskeleton in both cases, confirming labeling\nspecificity and robustness (Fig. R10h). Quantification of fluorescence intensity\nfurther demonstrated an appropriate ~2-fold increase (1.93 ×) in the Flag:HA\nsignal ratio in 2Flag-MAP7-HA cells compared to Flag-MAP7-HA (Fig. R10i).\nConsistently, STORM imaging of dual-tagged MAP7 showed that the average\nlocalization ratio of Flag to HA closely matched the predicted value (1.79 × vs.\n2.00 ×; Fig. R10j, k). Thus, although uncertainties in antibody recognition and\ndye photophysics remain—as the reviewer rightly pointed out—these new\nexperiments conceptually validate the quantitative potential of antibody-based\nSTORM when combined with high-quality reagents and appropriate calibration\ncontrols.\nmicrotubule-binding protein MAP7 as a calibration reference. g Design of\nthe MAP7-based calibration system. HeLa cells expressing Flag-MAP7-HA or\nFlag-MAP7-P2A-Flag-MAP7-HA (“2Flag-MAP7-HA”) are expected to display\nFlag:HA stoichiometries of 1:1 and 2:1, respectively. h-k Dual-color confocal (h)\nand STORM (j) images of HeLa cells expressing Flag-MAP7-HA or 2Flag-\nMAP7-HA, immunolabeled with anti-Flag (purple) and anti-HA (green)\nantibodies. Quantification of fluorescence intensity (i; n = 40 fields per group)\nand localization counts (k; n = 45 structures per group) confirmed Flag:HA ratios\nmatched expected values. Insets show dual-labeled tubulin with calculated\nFlag:HA ratios. Scale bars, 10 µm (overview) and 300 nm (insets).\nimages serve as supportive visualization rather than direct quantitative\nvalidation of the model. However, these images are still important in\nestablishing the nanoscale colocalization of RIP1, RIP3, and MLKL within\nnecrosomes. To provide direct experimental validation of our modeling\npredictions, we leveraged the MAP7 calibration and incorporated phospho-\nspecific monoclonal antibodies to assess the activation status of RIP3 or MLKL\nwithin individual necrosomes of measurable RIP1:RIP3 stoichiometry, thereby\nstrengthening the experimental grounding of our mathematical model.\nSpecifically, we performed three-color STORM imaging in TSZ-treated\nRIP1/MLKL double-knockout HeLa cells expressing Flag-RIP1 and HA-RIP3\n(Fig. R11l). The immunostaining procedures, antibody selection (e.g., anti-Flag\nand anti-HA), and imaging/analysis workflows were identical to those used in\nMAP7 calibration, ensuring quantitative comparability. Using MAP7 as an\ninternal reference, we determined the RIP3:RIP1 stoichiometry of individual\nnecrosomes at 4 h TSZ treatment (Fig. R11m), which ranged from 0.6 to 10,\nwith a mean of 3.97. To probe necrosome signaling output, we quantified RIP3\nactivation within each necrosome. The experimental distribution of p-RIP3\nclosely mirrors the model prediction (original Fig. 3e). p-RIP3 remains near\nbaseline when RIP3:RIP1 ≤2, exhibits a sharp, switch-like rise as the ratio\napproaches ~3, and then plateaus near maximal levels for ≥3. This\nconcordance demonstrates that the RIP3:RIP1 stoichiometric ratio functions as\na critical determinant, triggering pronounced signal amplification and switch-like\nbehavior in necrosome activation.\nFig. R11 (Figure 3l and 3m in revised manuscript) Quantitative STORM of\nnecrosomes in MLKL-deficient cells. l Three-color STORM of necrosomes\nin HA-RIP1/Flag-RIP3–expressing RIP1-MLKL-DKO HeLa cells treated with\nTSZ for 4 h, labeled for HA-RIP1 (green), Flag-RIP3 (purple), and p-RIP3\n(yellow). Insets highlight necrosome morphology with calculated Flag:HA ratios\n57\nand p-RIP3 localizations. m Quantification of RIP3:RIP1 stoichiometry and\nrelative p-RIP3 levels (localizations) in individual necrosomes (n = 89). Scale\nbars: 10 µm (overview), 300 nm (insets).\nSimilarly, we repeated three-color STORM imaging in TSZ-treated RIP1-KO\nHeLa cells expressing Flag-RIP1 and HA-RIP3, except for the usage of p-MLKL\nmonoclonal antibody (Fig. R12i). Experimental quantification of p-MLKL as a\nfunction of the RIP3:RIP1 ratio (Fig. R12j) exhibits a clear unimodal distribution,\npeaking at a ratio of approximately 3. This trend is closely recapitulated by the\nmodel simulation, which predicts maximal MLKL phosphorylation when each\nsubunit incorporates ~3 RIP3 molecules. This concordance between\nexperimental and simulated data supports a model in which stoichiometric\nbalance between RIP3 and RIP1 is critical for efficient necrosome activation.\nFig. R12 (Figure 4i and 4j in revised manuscript) Quantitative STORM of\nnecrosomes in MLKL-proficient cells. I Three-color STORM of necrosomes\nin HA-RIP1/Flag-RIP3–expressing RIP1-KO HeLa cells treated with TSZ for 4\nh, labeled for HA-RIP1 (green), Flag-RIP3 (purple), and p-MLKL (yellow). Insets\nhighlight necrosome morphology with calculated Flag:HA ratios and p-MLKL\nlocalizations. j Quantification of RIP3:RIP1 stoichiometry and relative p-MLKL\nlevels (localizations) in individual necrosomes (n = 122). Scale bars: 10 µm\n(overview), 300 nm (insets).\nIn summary, while the reviewer’s concerns about dye and antibody artifacts are\nfully justified, we mitigated them through careful experimental design, validated\nour approach with a MAP7 internal calibration reference, and incorporated\nfunctional readouts (p-RIP3, p-MLKL) to strengthen our conclusions. The\nrevised manuscript (Figs. 3l, 3m, 4i, and 4j) now presents direct experimental\nsupport for the stoichiometry-dependent necrosome activation model,\nalongside expanded explanations in the Results and Discussion sections.\nexperimental validation from the imaging data. The absence of consistent\nnanoscale colocalization among RIP1, RIP3, and MLKL, combined with\nunresolved technical artifacts in multi-color STORM, critically undermines the\ncredibility of the model .\nResponse: We thank the reviewer for the critical comments on Figure 8. In\nresponse to the concerns about the lack of direct imaging support for the model\nand potential multi-color STORM artifacts, we performed a comprehensive\nupgrade from methods to analysis in the revised manuscript. We appreciate the\nopportunity to briefly clarify how these updates yielded direct, reproducible\nevidence that supports the three conceptual panels summarized in Figure 8.\nFirst, to better extract the valuable quantitative information from our STORM\nimages, we shifted quantification from “length” to “area/localization counts” and\nimplemented a SMAP platform, which integrates >200 modules for localization\nfitting, drift correction, postprocessing, and spatial clustering (Fig. R7).\nLocalization data were subjected to Voronoi-based clustering in POCA, from\nwhich we extracted cluster-level parameters, including area and localization\ncounts. Using this powerful analysis pipeline, we re-analyzed the datasets and\nconfirmed that the key trends reproducibly recapitulated our original\nconclusions (Figs. R8 and R9).\nSecond, to address uncertainties in dye photophysics and antibody\nstoichiometry, we established an internal calibration system using microtubule-\nassociated protein MAP7, tagged with Flag and HA. Constructs with defined\ntheoretical stoichiometries (1:1 and 2:1) yielded expected ratios when assessed\nby fluorescence intensity (~1.93×) and localization counts (~1.79×), validating\nthis antibody-based STORM quantification strategy (Fig. R10). While technical\nuncertainties remain, this calibration demonstrates that dual-color STORM can\nbe used to derive reliable relative stoichiometries when paired with proper\ncontrols.\nThird, three-color STORM imaging in Figure 4e shows that RIP1, RIP3, and\nMLKL clearly co-occupy the same rod-like necrosome domain, providing direct\nevidence for nanoscale colocalization and spatial architecture of cellular\nnecrosomes. More importantly, taking advantage of the MAP7-based\ncalibration framework, we successfully measured RIP1:RIP3 stoichiometries\n59\nand linked them directly to p-RIP3 and p-MLKL accumulation at the single-\ncomplex level (Figs. R11 and R12). The observed switch-like increases in\nphosphorylation at ~3 RIP3 per RIP1 closely matched model predictions,\nproviding experimental evidence for the stoichiometry-dependent necrosome\nactivation model.\nFinally, we explicitly close the loop with the three panels in Figure 8. For the left\npanel (size–amount trade-off), Area/localization-based quantification under\nTNF, RIP3, and MLKL perturbations reproduced complementary changes in\nnecrosome number and size, consistent with constrained nucleation–growth\nlogic (revised Fig. 5). For the middle panel (RIP3 polymerization threshold and\noptimal configuration), both deterministic and stochastic models predict a peak\nin MLKL phosphorylation efficiency at ~1:3 RIP1:RIP3; the in situ stoichiometry\ntogether with the size–amount curves jointly support this threshold window and\nsaturation behavior (revised Fig. 4). For the right panel (C8 and c-FLIP\nassociated with RIP1 complex states), new imaging controls show that\ncaspase-8 recruitment and c-FLIP occupancy segregate with specific RIP1\nassembly states, supporting the panel’s mapping of the C8/c-FLIP axis to RIP1\ncomplex composition (revised Fig. 7).\nFig. R18 (Figure 8 in revised manuscript). Flexible and multi-strategic\n60\nassembly of death complexes in TNF signaling.\nNo single method can currently resolve necrosome architecture, stoichiometry,\nand function simultaneously: multi-color quantitative STORM is limited by dye\nphotophysics and antibody binding; bulk IP-MS captures composition but not\nsingle-complex heterogeneity. Our integrative strategy—combining IP-MS for\nensemble stoichiometry, STORM for nanoscale architecture, and modeling for\nsystems-level interpretation—provides a cross-validated framework with\nquantitative controllability and mechanistic interpretability. We recognize that\nchallenges remain, including dye-dependent artifacts, localization crowding,\nand restricted spatiotemporal sampling, and emphasize that continued\nadvances in super-resolution imaging will further refine and extend the insights\npresented here.\nThe figure numbers have been changed as summarized in the table below.\nBefore After\n1c revised 1c\n2c revised 2c\nnew 2d\n2d 2e\n2e 2f\n2f revised 2g\n3f 3d\n3g 3e\n3h 3f\nnew 3g\nnew 3h\nnew 3i\n61\nnew 3j\nnew 3k\nnew 3l\nnew 3m\nnew 4i\nnew 4j\nnew 5j\nnew 5k\nnew 5l\nnew 5m\n7e new 7e\n7f new 7f\n7g, h new 7g\n8 revised 8\nSup. 1 revised Sup. 1\nSup. 3 revised Sup. 3\nnew Sup. 7\nReferences:\n1. Baddeley D, Bewersdorf J. Biological Insight from Super-Resolution Microscopy:\nWhat We Can Learn from Localization-Based Images. Annu Rev Biochem. 2018 Jun\n20;87:965-989.\n2. Chen X, Zhu R, Zhong J, Ying Y, Wang W, Cao Y, Cai H, Li X, Shuai J, Han J. Mosaic\ncomposition of RIP1-RIP3 signalling hub and its role in regulating cell death. Nat Cell\nBiol. 2022 Apr;24(4):471-482.\n3. Feoktistova M, Geserick P, Kellert B, Dimitrova DP, Langlais C, Hupe M, Cain K,\nMacFarlane M, Häcker G, Leverkus M. cIAPs block Ripoptosome formation, a\nRIP1/caspase-8 containing intracellular cell death complex differentially regulated by\n62\ncFLIP isoforms. Mol Cell. 2011 Aug 5;43(3):449-63.\n4. Ferro LS, Fang Q, Eshun-Wilson L, Fernandes J, Jack A, Farrell DP, Golcuk M,\nHuijben T, Costa K, Gur M, DiMaio F, Nogales E, Yildiz A. Structural and functional\ninsight into regulation of kinesin-1 by microtubule-associated protein MAP7. Science.\n5. Fox JL, Hughes MA, Meng X, Sarnowska NA, Powley IR, Jukes-Jones R, Dinsdale D,\nRagan TJ, Fairall L, Schwabe JWR, Morone N, Cain K, MacFarlane M. Cryo-EM\nstructural analysis of FADD:Caspase-8 complexes defines the catalytic dimer\narchitecture for co-ordinated control of cell fate. Nat Commun. 2021 Feb 5;12(1):819.\n6. Kim C, Kang N, Min S, Thangam R, Lee S, Hong H, Kim K, Kim SY, Kim D, Rha H,\nTag KR, Lee HJ, Singh N, Jeong D, Hwang J, Kim Y, Park S, Lee H, Kim T, Son SW,\nPark S, Karamikamkar S, Zhu Y, Hassani Najafabadi A, Chu Z, Sun W, Zhao P, Zhang\nK, Bian L, Song HC, Park SG, Kim JS, Lee SY, Ahn JP, Kim HK, Zhang YS, Kang H.\nModularity-based mathematical modeling of ligand inter-nanocluster connectivity for\nunraveling reversible stem cell regulation. Nat Commun. 2024 Dec 23;15(1):10665.\n7. Levet F and Sibarita JB. PoCA: a software platform for point cloud data visualization\nand quantification. Nat Methods. 2023 May;20(5):629-630.\n8. Li J, McQuade T, Siemer AB, Napetschnig J, Moriwaki K, Hsiao YS, Damko E, Moquin\nD, Walz T, McDermott A, Chan FK, Wu H. The RIP1/RIP3 necrosome forms a\nfunctional amyloid signaling complex required for programmed necrosis. Cell. 2012\nJul 20;150(2):339-50.\n9. Liu J, Wu XL, Zhang J, Li B, Wang HY, Wang J, Lu JX. The structure of mouse RIPK1\nRHIM-containing domain as a homo-amyloid and in RIPK1/RIPK3 complex. Nat\nCommun. 2024 Aug 14;15(1):6975.\n10. Liu S, Liu H, Johnston A, Hanna-Addams S, Reynoso E, Xiang Y, Wang Z. MLKL forms\ndisulfide bond-dependent amyloid-like polymers to induce necroptosis. Proc Natl Acad\nSci U S A. 2017 Sep 5;114(36):E7450-E7459.\n11. Masullo LA, Kowalewski R, Honsa M, Heinze L, Xu S, Steen PR, Grabmayr H,\nPachmayr I, Reinhardt SCM, Perovic A, Kwon J, Oxley EP, Dickins RA, Bastings MMC,\nParish IA, Jungmann R. Spatial and stoichiometric in situ analysis of biomolecular\noligomerization at single-protein resolution. Nat Commun. 2025 May 6;16(1):4202.\n12. Meng Y, Davies KA, Fitzgibbon C, Young SN, Garnish SE, Horne CR, Luo C, Garnier\nJM, Liang LY, Cowan AD, Samson AL, Lessene G, Sandow JJ, Czabotar PE, Murphy\nJM. Human RIPK3 maintains MLKL in an inactive conformation prior to cell death by\nnecroptosis. Nat Commun. 2021 Nov 22;12(1):6783.\n13. Mompean M, Li W, Li J, Laage S, Siemer AB, Bozkurt G, Wu H, McDermott AE. The\nStructure of the Necrosome RIPK1-RIPK3 Core, a Human Hetero-Amyloid Signaling\n14. Ries J. SMAP: a modular super-resolution microscopy analysis platform for SMLM\ndata. Nat Methods. 2020 Sep;17(9):870-872.\n15. Shinohara H, Behar M, Inoue K, Hiroshima M, Yasuda T, Nagashima T, Kimura S,\nSanjo H, Maeda S, Yumoto N, Ki S, Akira S, Sako Y, Hoffmann A, Kurosaki T, Okada-\nHatakeyama M. Positive feedback within a kinase signaling complex functions as a\nswitch mechanism for NF-kB activation. Science. 2014 May 16;344(6185):760-4.\n16. Sica M, Roussel M, Legembre P. CD95/Fas stoichiometry in future precision medicine.\nCell Death Differ. 2025 Apr 15.\n17. Tenev T, Bianchi K, Darding M, Broemer M, Langlais C, Wallberg F, Zachariou A,\nLopez J, MacFarlane M, Cain K, Meier P. The Ripoptosome, a signaling platform that\nassembles in response to genotoxic stress and loss of IAPs. Mol Cell. 2011 Aug\n5;43(3):432-48.\n18. Wang L, Du F, Wang X. TNF-alpha induces two distinct caspase-8 activation pathways.\nCell. 2008 May 16;133(4):693-703.\n19. Wu X, Ma Y, Zhao K, Zhang J, Sun Y, Li Y, Dong X, Hu H, Liu J, Wang J, Zhang X, Li\nB, Wang H, Li D, Sun B, Lu J, Liu C. The structure of a minimum amyloid fibril core\nformed by necroptosis-mediating RHIM of human RIPK3. Proc Natl Acad Sci U S A.\n20. Wu XL, Hu H, Dong XQ, Zhang J, Wang J, Schwieters CD, Liu J, Wu GX, Li B, Lin JY,\nWang HY, Lu JX. The amyloid structure of mouse RIPK3 (receptor interacting protein\nkinase 3) in cell necroptosis. Nat Commun. 2021 Mar 12;12(1):1627.\n21. Wu YL, Tschanz A, Krupnik L, Ries J. Quantitative Data Analysis in Single-Molecule\nLocalization Microscopy. Trends Cell Biol. 2020 Nov;30(11):837-851.\n22. Xie T, Peng W, Yan C, Wu J, Gong X, Shi Y. Structural insights into RIP3-mediated\nnecroptotic signaling. Cell Rep. 2013 Oct 17;5(1):70-8.\nReviewers' Comments:\nReviewer #1 (Remarks to the Author):\nThe authors addressed my questions adequately.\nResponse: We thank the reviewer for their positive feedback and are glad that\nour revisions have satisfactorily addressed your concerns.\nReviewer #2 (Remarks to the Author):\nThe authors have sufficiently addressed all my questions. I'd like to recommend\nits publication in the present form.\nResponse: We thank the reviewer for their positive feedback and\nrecommendation. We are pleased that our revisions have satisfactorily\naddressed your concerns.\nReviewer #3 (Remarks to the Author):\nI am truly impressed by the revision. Of note, the MAP7-based calibration\nsystem helps a lot to solidify the conclusion. It is well done.\nResponse: We sincerely thank the reviewer for the generous feedback. We are\ndelighted that the MAP7-based calibration system and our revisions have\nstrengthened the conclusions, and we greatly appreciate your thoughtful\nevaluation and encouragement.\nI only have a minor concern about Figure 8. In this model, it may be read that\nupon TNF, when there is no caspase-8, the cell undergoes necroptosis, while\nwhen there is no RIPK3, the cell undergoes apoptosis. This is simply untrue in\nbiology, as many cells, including HeLa, which doesn't express RIPK3, TNF\nalone treatment cannot engage apoptosis. Also, the reason for caspase-8\ndeficiency that can support necroptosis is not only the reason presented in this\nfigure, e.g., RIPK1/3 can serve as a caspase-8 substrate.\nThe authors need to revise this model figure to (of course) highlight this unique\nwork, but should also follow the biology published by others in our community.\nResponse: We thank the reviewer for this insightful comment. We agree that\nthe original schematic in Figure 8 could be misinterpreted and did not fully\nreflect the complexity of TNF-induced cell death signaling. Indeed, TNF\nstimulation alone is typically insufficient to induce apoptosis; additional\nsensitizing conditions, such as cIAP1/2 depletion, inhibition of IKK/TBK1, or\nprotein synthesis blockade, are usually required. Likewise, caspase-8\ndeficiency promotes necroptosis through multiple mechanisms, including the\nrelease of RIP1 and RIP3 from caspase-8–mediated proteolytic regulation,\nthereby allowing their assembly into necroptotic complexes. In response to this\ncomment, we have revised Figure 8 and its legend to better capture these\nnuances and to ensure that our model is consistent with established findings in\nthe field.\nFigure 8. Flexible and multi-strategic assembly of death complexes in TNF\nsignaling.\nThis paper should be accepted.\nResponse: We thank the reviewer for the encouraging comments that helped\nus improve the study. We greatly appreciate the reviewer’s support.\nReviewer #4 (Remarks to the Author):\nThe authors have effectively addressed the comments raised during the first\nround of review, resulting in a significantly improved manuscript. I now\nrecommend it for publication.\nResponse: We are pleased that our revisions have addressed all your\nconcerns, and we greatly appreciate your thoughtful evaluation and support.\nOne final point: I suggest the authors temper their claims regarding precise\nstoichiometric ratios, such as the reported 3:1 ratio of RIP3 to RIP1. As shown\nin Figure 3m and Figure 4j, the stoichiometry exhibits a broad distribution. Given\nthe inherent challenges in quantitative super-resolution imaging, such as\nvariability in antibody labeling, dye blinking, and data analysis, accurate\nquantification remains difficult in STORM. Therefore, the authors should\nexercise caution when presenting conclusions based on exact numerical values.\nResponse: We thank the reviewer for this insightful comment. Although our\nMAP7-based calibration provides experimental validation that antibody-based\nSTORM can achieve sufficient quantitative accuracy under controlled\nconditions, we fully acknowledge that determining absolute molecular\nstoichiometry remains technically challenging. Variability in antibody labeling,\ndye blinking, and complex cellular environments can all influence quantitative\nprecision. Thus, further methodological advances are required to clearly define\nthe spatiotemporal organization of signalosomes at the resolution of single\ncomplexes.\nWe also agree that the “~3:1” ratio of RIP3 to RIP1 reported in our study should\nnot be interpreted as a universal or fixed constant. Rather, it represents an\napproximate efficiency optimum observed under our experimental conditions.\nConsistent with this, our large-scale stochastic simulations (Fig. 6) reproduced\nthe same dynamic behaviors predicted by the deterministic model, suggesting\nthat while the exact numerical value may vary across systems and contexts,\nthe underlying assembly principles are conserved. Accordingly, we have\nrevised the Discussion to include the following statement:\n“In our system, the apparent ~3:1 ratio of RIP3 to RIP1 reflects an average\nefficiency optimum rather than a universal stoichiometric constant. While the\nprecise position of this optimum may vary across cell types and experimental\ncontexts due to differences in expression levels, receptor organization, or\nstimulation strength, our large-scale stochastic simulations indicate that this\noptimal window of signaling efficiency is quantitatively flexible across biological\nsystems but mechanistically conserved.”\nWith these revisions, we hope we have satisfactorily addressed all reviewers’\ncomments. We sincerely thank the reviewers for their constructive, professional,\nand helpful suggestions, and we are grateful to the editor for handling our\nmanuscript."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors addressed my questions adequately.", "reviewer_2": "The authors have sufficiently addressed all my questions. I'd like to recommend its publication in the present form.", "reviewer_3": "I am truly impressed by the revision. Of note, the MAP7-based calibration system helps a lot to solidify the conclusion. It is well done.\nI only have a minor concern about Figure 8. In this model, it may be read that upon TNF, when there is no caspase-8, the cell undergoes necroptosis, while when there is no RIPK3, the cell undergoes apoptosis. This is simply untrue in biology, as many cells, including HeLa, which doesn't express RIPK3, TNF alone treatment cannot engage apoptosis. Also, the reason for caspase-8 deficiency that can support necroptosis is not only the reason presented in this figure, e.g., RIPK1/3 can serve as a caspase-8 substrate.\nThe authors need to revise this model figure to (of course) highlight this unique work, but should also follow the biology published by others in our community.\nThis paper should be accepted.", "reviewer_4": "The authors have effectively addressed the comments raised during the first round of review, resulting in a significantly improved manuscript. I now recommend it for publication.\nOne final point: I suggest the authors temper their claims regarding precise stoichiometric ratios, such as the reported 3:1 ratio of RIP3 to RIP1. As shown in Figure 3m and Figure 4j, the stoichiometry exhibits a broad distribution. Given the inherent challenges in quantitative super-resolution imaging, such as variability in antibody labeling, dye blinking, and data analysis, accurate quantification remains difficult in STORM. Therefore, the authors should exercise caution when presenting conclusions based on exact numerical values."}, "author_response": "Point-by-Point response to referees\nReviewers' Comments:\nReviewer #1 (Remarks to the Author):\nThe authors addressed my questions adequately.\nResponse: We thank the reviewer for their positive feedback and are glad that\nour revisions have satisfactorily addressed your concerns.\nReviewer #2 (Remarks to the Author):\nThe authors have sufficiently addressed all my questions. I'd like to recommend\nits publication in the present form.\nResponse: We thank the reviewer for their positive feedback and\nrecommendation. We are pleased that our revisions have satisfactorily\naddressed your concerns.\nReviewer #3 (Remarks to the Author):\nI am truly impressed by the revision. Of note, the MAP7-based calibration\nsystem helps a lot to solidify the conclusion. It is well done.\nResponse: We sincerely thank the reviewer for the generous feedback. We are\ndelighted that the MAP7-based calibration system and our revisions have\nstrengthened the conclusions, and we greatly appreciate your thoughtful\nevaluation and encouragement.\nI only have a minor concern about Figure 8. In this model, it may be read that\nupon TNF, when there is no caspase-8, the cell undergoes necroptosis, while\nwhen there is no RIPK3, the cell undergoes apoptosis. This is simply untrue in\nbiology, as many cells, including HeLa, which doesn't express RIPK3, TNF\nalone treatment cannot engage apoptosis. Also, the reason for caspase-8\ndeficiency that can support necroptosis is not only the reason presented in this\nfigure, e.g., RIPK1/3 can serve as a caspase-8 substrate.\nThe authors need to revise this model figure to (of course) highlight this unique\nwork, but should also follow the biology published by others in our community.\nResponse: We thank the reviewer for this insightful comment. We agree that\nthe original schematic in Figure 8 could be misinterpreted and did not fully\nreflect the complexity of TNF-induced cell death signaling. Indeed, TNF\nstimulation alone is typically insufficient to induce apoptosis; additional\nsensitizing conditions, such as cIAP1/2 depletion, inhibition of IKK/TBK1, or\nprotein synthesis blockade, are usually required. Likewise, caspase-8\ndeficiency promotes necroptosis through multiple mechanisms, including the\nrelease of RIP1 and RIP3 from caspase-8–mediated proteolytic regulation,\nthereby allowing their assembly into necroptotic complexes. In response to this\ncomment, we have revised Figure 8 and its legend to better capture these\nnuances and to ensure that our model is consistent with established findings in\nthe field.\nFigure 8. Flexible and multi-strategic assembly of death complexes in TNF\nsignaling.\nThis paper should be accepted.\nResponse: We thank the reviewer for the encouraging comments that helped\nus improve the study. We greatly appreciate the reviewer’s support.\nReviewer #4 (Remarks to the Author):\nThe authors have effectively addressed the comments raised during the first\nround of review, resulting in a significantly improved manuscript. I now\nrecommend it for publication.\nResponse: We are pleased that our revisions have addressed all your\nconcerns, and we greatly appreciate your thoughtful evaluation and support.\nOne final point: I suggest the authors temper their claims regarding precise\nstoichiometric ratios, such as the reported 3:1 ratio of RIP3 to RIP1. As shown\nin Figure 3m and Figure 4j, the stoichiometry exhibits a broad distribution. Given\nthe inherent challenges in quantitative super-resolution imaging, such as\nvariability in antibody labeling, dye blinking, and data analysis, accurate\nquantification remains difficult in STORM. Therefore, the authors should\nexercise caution when presenting conclusions based on exact numerical values.\nResponse: We thank the reviewer for this insightful comment. Although our\nMAP7-based calibration provides experimental validation that antibody-based\nSTORM can achieve sufficient quantitative accuracy under controlled\nconditions, we fully acknowledge that determining absolute molecular\nstoichiometry remains technically challenging. Variability in antibody labeling,\ndye blinking, and complex cellular environments can all influence quantitative\nprecision. Thus, further methodological advances are required to clearly define\nthe spatiotemporal organization of signalosomes at the resolution of single\ncomplexes.\nWe also agree that the “~3:1” ratio of RIP3 to RIP1 reported in our study should\nnot be interpreted as a universal or fixed constant. Rather, it represents an\napproximate efficiency optimum observed under our experimental conditions.\nConsistent with this, our large-scale stochastic simulations (Fig. 6) reproduced\nthe same dynamic behaviors predicted by the deterministic model, suggesting\nthat while the exact numerical value may vary across systems and contexts,\nthe underlying assembly principles are conserved. Accordingly, we have\nrevised the Discussion to include the following statement:\n“In our system, the apparent ~3:1 ratio of RIP3 to RIP1 reflects an average\nefficiency optimum rather than a universal stoichiometric constant. While the\nprecise position of this optimum may vary across cell types and experimental\ncontexts due to differences in expression levels, receptor organization, or\nstimulation strength, our large-scale stochastic simulations indicate that this\noptimal window of signaling efficiency is quantitatively flexible across biological\nsystems but mechanistically conserved.”\nWith these revisions, we hope we have satisfactorily addressed all reviewers’\ncomments. We sincerely thank the reviewers for their constructive, professional,\nand helpful suggestions, and we are grateful to the editor for handling our\nmanuscript."}]

Decision letter