eLife Assessment

This manuscript provides valuable insight into how genome organization changes as cells progress through the cell cycle after mitotic exit, identifying two sharp genome remodeling events at G1-S and to a lesser extent, at S-G2 transitions. The conclusions are supported by solid, rigorous data, including sequencing and orthogonal imaging data. The use of sorted unsynchronized cells rather than cells treated with drugs is a particular strength.

Reviewer #1 (Public review):

This work convincingly shows that, rather than gradually "evolving" throughout interphase, global chromatin architecture undergoes unexpectedly sharp remodeling at G1-S (and to a lesser extent, S-G2) transitions. By applying "standard" Hi-C analyses on carefully sorted cells, the authors provide an excellent temporal view of how global chromatin architecture is changed throughout the cell cycle. They show a surprisingly abrupt increase in compartmentation strength (particularly interactions between the "active" A compartments) at G1-S transition, which is slightly weakened at S-G2 transition. Follow-up experiments show convincingly that the compartment "maturation" does not require the DNA synthesis accompanying S phase per se, but the authors have not identified the responsible factors (work for future publications). The possible biological ramifications of these architectural changes (setting up potential replication "factories", and/or facilitating transcription-replication conflict resolution, both more pertinent for the active A compartments, which are most affected) have been well discussed in the article, but still remain speculative at this stage.

My major criticism of this article is aimed more at the state of the field in general, rather than this specific article, but it should be discussed to give a more balanced view: what actually is a chromatin compartment? Chromosomal tracing and live tracking experiments have shown that the majority of "structures" identified from Hi-C experiments are statistical phenomena, with even "strong" interactions only being infrequent and transient. A-B compartments are "built up" from multiple very low-frequency "interactions", so ascribing causal effects for genome functions is even tougher. As a result, I have very little confidence in the results of the authors' polymer simulations and their inferred "peninsula" A compartment structures without any other supporting experimental data.

Comments on revised version.

The authors have included orthogonal DNA FISH evidence to support their claims which greatly strengthens the manuscript. Their further precisions within the discussion have answered all of my previous concerns with the manuscript.

Reviewer #2 (Public review):

Summary:

This manuscript by Choubani et al presents a technically strong analysis of A/B compartment dynamics across interphase using cell-cycle-resolved Hi-C. By combining the elegant Fucci-based staging system with in situ Hi-C, the authors achieve unusually fine temporal resolution across G1, S, and G2, particularly within the short G1 phase of mESCs. The central finding that A/B compartment strength increases abruptly at the G1/S transition, stabilizes during S phase, and subsequently weakens toward G2 challenges the prevailing view that compartmentalization strengthens monotonically throughout interphase. The authors further propose that this "compartment maturation" is triggered by S-phase entry but occurs independently of active DNA synthesis, and that it involves a consolidation and large-scale reorganization of A-compartment domains.

Strengths:

Overall, this is a thoughtfully executed study that will be of broad interest to the 3D genome community. The data are of high quality, and the analyses are extensive, albeit not completely novel. In particular, previous work (Nagano et al 2017 and Zhang et al 2019) has shown that compartments are re-established after mitosis and strengthened during early interphase, and single-cell Hi-C studies have reported changes in compartment association across S phase. In particular, Nagano et al show that DNA replication correlates with a build-up of compartments, similar to what is presented here, with the authors' conclusion that compartment strength peaks in early S. The idea that it weakens toward G2, rather than continuing to strengthen, appears to be novel and differs from the prevailing framing in the literature.

Comments on revised version.

The authors have responded constructively to my major conceptual concerns. The distinction between DNA synthesis and replication initiation has been clarified appropriately. The additional insulation analysis substantially strengthens the argument that compartment maturation is not simply a consequence of changing loop extrusion dynamics, although I would encourage slightly more cautious wording regarding "independence" from cohesin-mediated extrusion. The peninsula model is now framed appropriately as a heuristic interpretation and supported by orthogonal imaging data. Finally, the discussion of conservation across cell types has been appropriately tempered. Overall, I believe the manuscript has been significantly improved.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

This work convincingly shows that, rather than gradually "evolving" throughout interphase, global chromatin architecture undergoes unexpectedly sharp remodeling at G1-S (and to a lesser extent, S-G2) transitions. By applying "standard" Hi-C analyses on carefully sorted cells, the authors provide an excellent temporal view of how global chromatin architecture is changed throughout the cell cycle. They show a surprisingly abrupt increase in compartmentation strength (particularly interactions between the "active" A compartments) at G1-S transition, which is slightly weakened at S-G2 transition. Follow-up experiments show convincingly that the compartment "maturation" does not require the DNA synthesis accompanying S phase per se, but the authors have not identified the responsible factors (work for future publications). The possible biological ramifications of these architectural changes (setting up potential replication "factories", and/or facilitating transcription-replication conflict resolution, both more pertinent for the active A compartments, which are most affected) have been well discussed in the article, but still remain speculative at this stage.

We thank Reviewer #1 for their positive and constructive assessment of our work, and we agree that the questions of responsible factors and biological ramifications are important directions for future studies.

My major criticism of this article is aimed more at the state of the field in general, rather than this specific article, but it should be discussed to give a more balanced view: what actually is a chromatin compartment? Chromosomal tracing and live tracking experiments have shown that the majority of "structures" identified from Hi-C experiments are statistical phenomena, with even "strong" interactions only being infrequent and transient. A-B compartments are "built up" from multiple very low-frequency "interactions", so ascribing causal effects for genome functions is even tougher. As a result, I have very little confidence in the results of the authors' polymer simulations and their inferred "peninsula" A compartment structures without any other supporting experimental data.

We thank the reviewer for raising this important conceptual point. This issue extends beyond the scope of the present study but reflects an important ongoing discussion in the 3D genome field regarding the biological interpretation of chromatin compartments.

We agree that Hi-C interactions should not be interpreted as stable pairwise contacts present in every cell. A growing body of evidence from chromatin tracing and live-cell imaging studies has demonstrated that many chromatin interactions identified by Hi-C are probabilistic and dynamic, with substantial cell-to-cell variability. Relatively speaking, however, A/B compartment organization represents a robust population-level property of genome organization that is highly reproducible across biological replicates and closely correlates with multiple independent genomic features. In particular, replication timing (RT) correlates very well with A/B compartment organization, with early and late RT domains corresponding to A and B compartment domains, respectively.

Furthermore, single-cell DNA replication sequencing (scRepli-seq) analyses have revealed remarkably low cell-to-cell variability in RT, suggesting that RT profiles and A/B compartment organization reflect biologically meaningful and relatively stable features of nuclear architecture rather than purely statistical artifacts. Thus, while individual chromatin contacts may be transient and probabilistic, the megabase-scale compartment organization inferred from them appears sufficiently reproducible to support reproducible RT programs and other genome functions. Additional support comes from decades of work on DNA replication demonstrating that spatiotemporal replication patterns, visualized as replication foci following short EdU pulses, are remarkably reproducible between individual cells throughout S-phase progression. These patterns reveal clear spatial segregation between early-replicating A-compartment regions and late-replicating B-compartment regions even at the single-cell level.

To directly address the reviewer’s concern that A/B compartment organization might represent only an ensemble-level statistical phenomenon without biological relevance at the single-cell level, we performed L1/B1-EdU DNA FISH on asynchronous mESCs and MC12 embryonic carcinoma cells. L1 elements are enriched in B compartment domains, while B1 elements are enriched in A compartment domains, allowing visualization of compartment segregation in individual nuclei across the cell cycle. This single-cell analysis confirmed our Hi-C findings: compartment segregation increased from G1 to early S, remained elevated throughout S phase with reduced cell-to-cell variability, and then weakened in G2. Thus, compartment segregation is detectable in single cells, and the temporal dynamics of compartment maturation identified by population Hi-C were independently recapitulated at single-cell resolution. We have added a new Results section describing these findings titled “Stepwise A/B compartment reorganization during interphase is conserved at single-cell resolution”, including new Figure panels 2D–H and Figure S5.

Regarding the polymer simulations, we agree that these models should be interpreted with caution. We do not view them as direct representations of individual nuclei, but rather as heuristic models that help visualize structural trends present in the Hi-C data. To make this point explicit, we have added the following statement to the revised manuscript: “We note that these models are derived from population-averaged Hi-C data and should therefore be interpreted as a heuristic framework for understanding A/B compartment dynamics, rather than as definitive representations of individual nuclei.”

That said, we did try to provide orthogonal experimental support for the "A peninsula" model by performing DNA FISH. In brief, we measured distances between probe pairs spanning two A domains on chromosomes 2 and 15 across different cell-cycle stages. We observed significant increases in inter-probe distances from G1 to early/mid S, with the most pronounced changes involving the central probes (i.e., probes located near the domain center), consistent with physical extension of the A domain during S phase. While these data do not prove the exact geometry depicted by the model, these findings provide independent experimental support for the peninsula model as a simplified but biologically grounded interpretation of the Hi-C data. These results are described in the Results section titled “A-compartment consolidation during S-phase involves enhanced long-range contacts and structural reorganization” and are presented in new Figure panels 5D–F and Figure S12.

We thank the reviewer again for raising this important conceptual issue, which prompted us to better clarify both the biological interpretation and the limitations of our analyses.

Specific minor points:

(1) A better explanation for how Figure 1E was generated is required, because this figure could be very misleading. Figure 1F and all other cis-decay plots (and the Hi-C maps themselves) show that the strongest interactions are always at smaller genomic separations, so why should there be more "heat" at the megabase ranges in Figure 1E?

We appreciate the reviewer's observation. The apparent discrepancy is simply due to the fact that the decay plot (Fig. 1E in the original submission, now Fig. S2C) does not include the shortest-range interactions. The lowest distance plotted is 25 kb, following the method originally described in Nagano et al. (Nature, 2017), which we used as a reference. The shortest-range interactions (below 25 kb) are indeed the most enriched, as seen on the diagonal of the Hi-C maps (Fig. 2A) and in the standard cis-decay plot (Fig. 1F in the original submission, now Fig. S2F). With the 25 kb cutoff in place, the "heat" observed at megabase distances (specifically 12–50 Mb) in early/mid G1 corresponds to the dark, non‑specific band around the diagonal visible in the Hi-C maps at the same time points. This is also reflected in the cis-decay plot (Fig. S2F), where distances in that range appear above the expected curve (a "bump" rather than a linear decay).

To avoid confusion, we have updated the figure legend accordingly (Fig. S2C): “(C) Contact decay profiles for all cell cycle phases, plotted from 25 kb to 50 Mb, illustrating a continuum of cis-interactions and a progressive shift from long-range (> 12 Mb) to short-range (< 1 Mb) interactions during the G1-to-S phase transition.”

We hope this explanation clarifies the figure.

(2) An ultra-high-resolution Hi-C study (Harris et al., Nat Commun, 2023) identified very small A and B compartments, including distinctions between gene promoters and gene bodies, raising further questions as to what the nature of a compartment really is beyond a statistical phenomenon. It is unreasonable to expect the authors to generate maps as deep as this prior study, but how much do their conclusions change according to the resolution of their compartment calling? The authors should include a balanced discussion on the "meaning" of A/B compartments.

We thank the reviewer for highlighting recent ultra-high-resolution work, such as Harris et al. (Nat Commun, 2023), which reveals compartment-like features at much finer genomic scales. We agree that these findings raise important questions regarding the scale-dependence and interpretation of A/B compartmentalization.

In our study, we specifically focus on coarse-grained compartment organization, analyzed across multiple resolutions (from ~1 Mb to sub‑megabase scales). Importantly, the key conclusions, including the abrupt strengthening of compartmentalization at the G1/S transition, are robust across these resolutions.

We also note that fine-scale compartment-like features likely operate under different rules than larger-scale compartments. Recent evidence suggests that these "micro‑compartments" are more dynamic and transient (Harris et al., Nat Commun, 2023; Goel et al., Nat Struct Mol Biol, 2025), whereas the large-scale compartments analyzed here capture more stable, global segregation patterns. Understanding how these two regimes relate to one another remains an important open question.

We have added the following statement in the Discussion acknowledging the scale-dependent nature of compartmentalization: “At the same time, recent ultra-high-resolution Hi-C studies [36,37] have revealed compartment-like features at much finer genomic scales, emphasizing that A/B compartmentalization is, to some extent, inherently scale-dependent. Understanding how these fine-scale, often transient micro-compartments relate to the more stable, large-scale segregation patterns described here will be an important direction for future studies.”

Reviewer #2 (Public review):

Summary:

This manuscript by Choubani et al presents a technically strong analysis of A/B compartment dynamics across interphase using cell-cycle-resolved Hi-C. By combining the elegant Fucci-based staging system with in situ Hi-C, the authors achieve unusually fine temporal resolution across G1, S, and G2, particularly within the short G1 phase of mESCs. The central finding that A/B compartment strength increases abruptly at the G1/S transition, stabilizes during S phase, and subsequently weakens toward G2 challenges the prevailing view that compartmentalization strengthens monotonically throughout interphase. The authors further propose that this "compartment maturation" is triggered by S-phase entry but occurs independently of active DNA synthesis, and that it involves a consolidation and large-scale reorganization of A-compartment domains.

Strengths:

Overall, this is a thoughtfully executed study that will be of broad interest to the 3D genome community. The data are of high quality, and the analyses are extensive, albeit not completely novel. In particular, previous work (Nagano et al 2017 and Zhang et al 2019) has shown that compartments are re-established after mitosis and strengthened during early interphase, and single-cell Hi-C studies have reported changes in compartment association across S phase. In particular, Nagano et al show that DNA replication correlates with a build-up of compartments, similar to what is presented here, with the authors' conclusion that compartment strength peaks in early S. The idea that it weakens toward G2, rather than continuing to strengthen, appears to be novel and differs from the prevailing framing in the literature.

We thank Reviewer #2 for their thoughtful assessment and critique. We address their specific concerns below.

Weaknesses:

That said, several aspects of the conceptual framing and interpretation would also benefit from further clarification, and the mechanistic interpretation of the reported compartment dynamics requires more careful positioning relative to established models of genome organization. Specific concerns are outlined below:

(1) One of the major conclusions of the study is that compartment maturation does not require ongoing DNA replication. However, the interpretation would benefit from more precise wording. Thymidine arrest still permits licensing, replisome assembly, and other S-phase-associated chromatin changes upstream of bulk DNA synthesis. Therefore, their data, as presented, demonstrate independence from DNA synthesis per se, but not necessarily from the broader replication program. Please clarify this distinction in the text and interpretations throughout the manuscript.

We thank the reviewer for this important distinction. We agree with their point and have never claimed that compartment maturation is independent of the broader replication program. That is why we carefully used the term "active DNA synthesis" rather than "replication" throughout the manuscript.

However, we acknowledge that one sentence in the text was ambiguous. The original sentence read: “These results confirm that the cell population was successfully synchronized at the G1/S boundary, representing a pre-replicative state where replication had not yet initiated, although cell-cycle markers indicated entry into S-phase.”

We have now revised it to: “These results confirm that the cell population was successfully synchronized at the G1/S boundary, representing a state where the replication program (including origin licensing, replisome assembly, and helicase activation) has been initiated, as indicated by cell-cycle markers, but ongoing DNA synthesis (elongation) is blocked. ”

This clarifies that compartment maturation is independent of active DNA synthesis (elongation) but not necessarily independent of upstream replication-associated processes. The change has been made in the manuscript.

(2) A major conceptual issue that is not addressed at all is the well-established anti-correlation between cohesin-mediated loop extrusion and A/B compartmentalization. Numerous studies have shown that loss of cohesin or reduced loop extrusion leads to stronger compartment signals, whereas increased cohesin residence or enhanced extrusion weakens compartmentalization. Given this framework, an obvious alternative explanation for the authors' observations is that the abrupt increase in compartment strength at G1/S, and its decline toward G2, could reflect cell-cycle-dependent modulation of cohesin activity rather than a compartment-intrinsic "maturation" program.

The manuscript does not explicitly consider this possibility, nor does it examine loop extrusion-related features (such as loop strength, insulation, or stripe patterns) across the same cell-cycle stages. Without discussing or analyzing this widely accepted model, it is difficult to distinguish whether the reported compartment dynamics represent a novel architectural mechanism or an indirect consequence of known changes in extrusion behavior during the cell cycle. I strongly encourage the authors to analyze their data to determine if they observe anti-correlated loop changes at the same time they observe compartment changes. Ideally, the authors would remove loop extrusion during interphase using well-established cohesin degrons available in mESCs and determine if the relative differences in compartment dynamics persist.

We thank the reviewer for raising this interesting point. We agree that there is a well-established anti-correlation between cohesin-mediated loop extrusion and A/B compartment strength in the literature.

To test whether cell cycle compartment dynamics, particularly compartment maturation at the G1/S transition, could be explained by changes in loop extrusion, we analyzed insulation at RAD21/CTCF sites (mESC data from Hansen et al., eLife, 2017) across the cell cycle. During normal cycling, we indeed observed an anti-correlation: insulation dropped as compartment strength increased at the G1/S transition. However, in G1/S-arrested cells, insulation did not drop compared to late G1 (it even slightly increased) even though compartment maturation still occurred, indicating that the two processes can be uncoupled. This is consistent with other studies showing that loop extrusion and compartment dynamics are driven by independent mechanisms (Nora et al., Cell, 2017; Zhang et al., Nat Commun, 2021), although we cannot fully rule out some contribution from loop extrusion dynamics without direct cohesin degron experiments.

We have added a new Results section describing these findings titled “Compartment maturation is independent of cohesin-mediated loop extrusion”, including new Figure panels 3H, I, and Figure S7.

(3) The proposed "peninsula-like" A-domain structures are inferred from ensemble Hi-C data and polymer modeling, rather than directly observed physical conformations. That is, single-cell imaging data clearly have shown that Hi-C (especially ensemble Hi-C) cannot uniquely specify physical conformations and that different underlying structures can produce similar contact patterns. The "peninsula" language, as written, risks being interpreted as a literal structural model rather than a conceptual visualization. Instead of risking this as just another nuanced Hi-C feature in the field, the authors could strengthen the manuscript by either (i) explicitly framing the peninsula model as a heuristic description of contact redistribution rather than a definitive physical architecture, or (ii) discussing alternative structural scenarios that could give rise to similar Hi-C patterns. Clarifying this distinction would improve the rigor and help readers better understand what aspects of A-compartment consolidation are directly supported by the data versus model-based extrapolations. For example, it would be useful to clarify whether the observed increase in long-range A-A contacts reflects spatial extension of internal A regions, changes in loop extrusion dynamics, increased compartment mixing within the A state, or population-averaged heterogeneity across alleles.

We thank the reviewer for this important clarification. We agree that the "peninsula" model should be framed as a heuristic description. As detailed in our response to Reviewer #1 (see above), we have added a disclaimer to the manuscript and provided orthogonal DNA FISH support for physical extension of A-domains during S phase. We have also ensured that the language emphasizes the conceptual nature of the model.

(4) The extension of the analysis to additional cell types using HiRES single-cell data is a valuable addition and supports the idea that compartment maturation is not unique to mESCs. However, the limitations of these data, in particular, the limited phase resolution, in addition to the pseudo-bulk aggregation and variable coverage, should be emphasized more clearly in the main text. Framing these results as evidence for conservation in principle, rather than definitive proof of identical dynamics across tissues, would be a more appropriate framing.

We agree with the reviewer. We have already explicitly acknowledged the limited temporal resolution and variable coverage of the HiRES dataset in the main text. To better reflect its supporting role, we have moved the HiRES figure (previously Fig. 4) to Fig. S10 and merged the corresponding results section with the previous one titled: “Formation of a consolidated A compartment in S-phase”.

We have also revised the language to avoid overstatement. The original conclusion read: “Together, these findings strongly indicate that compartment maturation and the accompanying A compartment consolidation represent a robust and universally observed feature across different developmental contexts.”

This has been changed to: “Together, these findings support the notion that compartment maturation and the accompanying A-compartment consolidation are not unique to mESCs and may represent a broadly conserved feature of mammalian chromatin organization.”

Similarly, the abstract has been adjusted from: “Moreover, compartment maturation was not limited to mESCs but was also observed across different developmental contexts in mice.” to: “Moreover, compartment maturation was not limited to mESCs but was also evident across different developmental contexts in mice.”

These changes frame the results as evidence for conservation in principle rather than definitive proof of identical dynamics across tissues.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Please address the minor points in the public review.

In addition, on page 7, line 285: "In contrast, interactions showed minimal change across all distances though interphase". Do the authors mean "In contrast, B-B interactions..."?

We thank the reviewer for catching this. The sentence has been corrected.

eLife Assessment

This important study identifies PRRT2 as an auxiliary regulator of Nav channel slow inactivation in vitro and in vivo, proposing that PRRT2 facilitates entry into, and delays recovery from, the slow-inactivated state. The revised manuscript has been substantially strengthened, providing compelling evidence that PRRT2 is relevant to normal brain physiology and disease pathophysiology, providing a mechanistic link between PRRT2 mutations and episodic neurological phenotypes. Overall, this study will be of interest to ion channel biophysicists and neurophysiologists, particularly those studying channelopathies.

Reviewer #1 (Public review):

Summary:

The manuscript by Lu and colleagues demonstrate convincingly that PRRT2 interacts with brain voltage-gated sodium channels to enhance slow inactivation in vitro and in vivo. The work is interesting and rigorously conducted. The relevance to normal physiology and disease pathophysiology (e.g., PRRT2-related genetic neurodevelopmental disorders) seems high. Some simple additional experiments could elevate the impact and make the study more complete.

Strengths:

Experiments are conducted rigorously including experimenter blinding and appropriate controls. Data presentation is excellent and logical. The paper is well written for a general scientific audience.

Comments on revised version.

The manuscript by Lu and colleagues has been revised sufficiently to address all my prior concerns.

Experiments are conducted rigorously including experimenter blinding and appropriate controls. Data presentation is excellent and logical. The paper is well written for a general scientific audience.

Reviewer #2 (Public review):

Summary:

As a member of DspB subfamily, PRRT2 is predominantly expressed in CNS and has been associated with various paroxysmal neurological disorders. Previous studies have shown that PRRT2 interacts with Nav and Cav channels, modulating channel properties and neuronal excitability.

In this manuscript, Lu et al. demonstrate that PRRT2 is a potent regulator of Nav channel slow inactivation, promoting the development of Nav slow inactivation and impeding the recovery from slow inactivation. This effect is highly conserved in PRRT2s across species as well as among DspB family members (TRARG1 and TMEM233). The authors further confirmed the interaction between Nav channels and PRRT2 in heterologous expression systems as well as in Prrt2-V5 knock-in mice. Prrt2-mutant mice, which lack PRRT2 expression, require lower stimulation thresholds for evoking after-discharges when compared with WT mice.

Overall, this is a well-executed and methodologically comprehensive study. This work offers valuable insight into the physiological functions of PRRT2 and reveals a potential pathogenic mechanism underlying PRRT2-associated neurological disorders.

The revised manuscript has addressed most of the concerns raised by the reviewers and has been substantially strengthened, although I still have several concerns regarding the discussion section.

Strengths:

(1) Overall, this is a well-executed and methodologically comprehensive study. The electrophysiological data strongly support the conclusion that PRRT2 is a potent regulator of Nav channel slow inactivation. The observation that this regulation is conserved in PRRT2 across species and among DspB family members raises the possibility that altered regulation of Nav channels may also contribute to the pathogenesis of TRARG1- or TMEM233-associated disorders.

(2) Co-immunoprecipitation assay performed using brain tissue from genetically modified Prrt2-V5 knock-in mice provides convincing in vivo evidence for the interaction between PRRT2 and Nav1.2 channels.

(3) Prrt2-V5 KI mice show markedly reduced PRRT2 protein expression and display phenotypes similar to those observed in Prrt2-mutant mice, supporting an important role of PRRT2 in regulating neuronal and network excitability.

Weaknesses:

(1) Nav1.6 is also highly expressed in cortical neurons and is widely regarded as a major contributor to action potential initiation and sustained high-frequency firing. Given that PRRT2 similarly regulates the fast and slow inactivation of Nav1.6 and Nav1.2 channels, the potential contribution of Nav1.6 regulation to neuronal and network excitability should be discussed.

(2) Slow inactivation is generally considered to develop over timescales ranging from hundreds of milliseconds to seconds or longer. Therefore, the statement in Discussion (Page 13, line 381-382) that "slow inactivation develops on a timescale of tens of milliseconds to seconds" may not accurately reflect the conventional kinetic definition of slow inactivation and should be clarified.

(3) Page 14, line 417-430: "question about how Nav channel slow inactivation is regulated in cells that do not express PRRT2".<br /> PRRT2 is unlikely to be the sole regulator of Nav channel slow inactivation. Other molecules and signaling pathways may regulate Nav channel and contribute to neuronal excitability. In addition, neuronal excitability can also be regulated through modulating other Nav properties, such as long-term inactivation or slow recovery from inactivation, as well as through modulating the activity of other ion channels, for example, Kv7.2 and Kv7.3 channels. Therefore, PRRT2-negative cells may utilize alternative mechanisms to fine-tune neuronal excitability. In its current form, this paragraph somewhat overstates the role of PRRT2 and would benefit from a more balanced discussion.

(4) Page 50, Figure 7-figure supplement 2: It would be helpful to include representative traces of the 1st and the last (20th) compound APs in panels B and C.

Reviewer #3 (Public review):

This paper reveals that the neuronal protein PRRT2, previously known for its association with paroxysmal dyskinesia and infantile seizures, modulates the slow inactivation of voltage-gated sodium ion (Nav) channels, a gating process that limits excitability during prolonged activity. Using electrophysiology, molecular biology, and mouse models, the authors show that PRRT2 accelerates entry of Nav channels into the slow-inactivated state and slows their recovery, effectively dampening excessive excitability. The effect seems evolutionarily conserved, requires the C-terminal region of PRRT2, and is recapitulated in cortical neurons, where PRRT2 deficiency leads to hyper-responsiveness and reduced cortical resilience in vivo. These findings extend the functional repertoire of PRRT2, identifying it as a physiological brake on neuronal excitability. The work provides a mechanistic link between PRRT2 mutations and episodic neurological phenotypes.

Comments:

(1) The precise structural interface and the molecular basis of gating modulation remain inferred rather than demonstrated.

(2) The in vivo phenotype reflects a complex circuit outcome and does not isolate slow-inactivation defects per se.

(3) Expression of PRRT2 in muscle or heart is low, so the cross-isoform claims are likely of limited physiological significance.

(4) The mechanistic separation between trafficking of PRRT2 and its gating effects is not clearly resolved.

(5) Additional studies with Nav1.6 should be carried out.

Comments on revised version.

These comments have been addressed in the revised version.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

Summary:

The manuscript by Lu and colleagues demonstrates convincingly that PRRT2 interacts with brain voltage-gated sodium channels to enhance slow inactivation in vitro and in vivo. The work is interesting and rigorously conducted. The relevance to normal physiology and disease pathophysiology (e.g., PRRT2-related genetic neurodevelopmental disorders) seems high. Some simple additional experiments could elevate the impact and make the study more complete.

Strengths:

Experiments are conducted rigorously, including experimenter blinding and appropriate controls. Data presentation is excellent and logical. The paper is well written for a general scientific audience.

We thank the reviewer for these positive comments and for the thoughtful evaluation of our work.

Weaknesses:

There are a few missing experiments and one place where data are over-interpreted.

(1) An in vitro study of Nav1.6 is conspicuously absent. In addition to being a major brain Na channel, Nav1.6 is predominant in cerebellar Purkinje neurons, which the authors note lack PRRT2 expression. They speculate that the absence of PRRT2 in these neurons facilitates the high firing rate. This hypothesis would be strengthened if PRRT2 also enhanced slow inactivation of Nav1.6. If a stable Nav1.6 cell were not available, then simple transient co-transfection experiments would suffice.

We thank the reviewer for raising this point. In our previous work, PRRT2 produced broadly similar effects on Nav1.2 and Nav1.6. Therefore, in the initial version of this study, we focused primarily on Nav1.2 as a representative neuronal Nav channel isoform and placed greater emphasis on testing whether PRRT2-dependent regulation of slow inactivation extends across additional Nav isoforms.

We have now performed new heterologous expression experiments to test whether PRRT2 modulates Nav1.6 slow inactivation. Consistent with our findings for other Nav isoforms, PRRT2 significantly enhances the slow inactivation of Nav1.6. We have incorporated these data into the revised Results and Figures, please refer to Page 8, Lines 211-215; Figures 4E and J.

(2) To further demonstrate the physiological impact of enhanced slow inactivation, the authors should consider a simple experiment in the stable cell line experiments (Figure 1) to test pulse frequency dependence of peak Na current. One would predict that PRRT2 expression will potentiate 'run down' of the channels, and this finding would be complementary to the biophysical data.

We thank the reviewer for this helpful suggestion. In the revised manuscript, we performed a pulse-train protocol in the stable Nav1.2 cell line and quantified the use-dependent attenuation (“run-down”) of peak sodium current across successive depolarizations (Figure 1-figure supplement 1C). Compared with control cells, PRRT2-expressing cells exhibited a larger decline in peak current during trains, indicating greater reduction in channel availability during repetitive depolarizations (Figure 1-figure supplement 1C). This pattern is consistent with our observations above showing that PRRT2 enhances Nav channel slow inactivation. These new data have been incorporated into the revised manuscript. Please refer to Page 5, Lines 133-140; Figure 1-figure supplement 1C.

(3) The study of one K channel is limited, and the conclusion from these experiments represents an over-interpretation. I suggest removing these data unless many more K channels (ideally with measurable proxies for slow inactivation) were tested. These data do not contribute much to the story.

We agree with the reviewer’s assessment. To avoid over-interpretation and to maintain focus on PRRT2-dependent regulation of Nav channel slow inactivation, we have removed the potassium channel dataset and the associated conclusions from the revised manuscript.

(4) In Figure 2, the authors should confirm that protein is indeed expressed in cells expressing each truncated PRRT2 construct. Absent expression should be ruled out as an explanation for the enhancement of slow inactivation.

We thank the reviewer’s concern regarding expression of the truncated PRRT2 constructs in the Nav1.2 stable cell line, particularly PRRT2(1-266), which shows little effect on slow inactivation of Nav1.2 channels. In the revised manuscript, we conducted western blot to verify expression of the PRRT2(1-266)-HA construct in the Nav1.2 stable cell line. We have added these results to the revised manuscript, please refer to Page 6, Lines 171-173; Figure 2-figure supplement 1A and B.

Reviewer #2 (Public review):

Summary:

As a member of DspB subfamily, PRRT2 is primarily expressed in the nervous system and has been associated with various paroxysmal neurological disorders. Previous studies have shown that PRRT2 directly interacts with Nav1.2 and Nav1.6, modulating channel properties and neuronal excitability.

In this study, Lu et al. reported that PRRT2 is a physiological regulator of Nav channel slow inactivation, promoting the development of Nav slow inactivation and impeding the recovery from slow inactivation. This effect can be replicated by the C-terminal region (256-346) of PRRT2, and is highly conserved across species from zebrafish, mouse, to human PRRT2. TRARG1 and TMEM233, the other two DspB family members, showed similar effects on Nav1.2 slow inactivation. Co-IP data confirms the interaction between Nav channels and PRRT2. Prrt2-mutant mice, which lack PRRT2 expression, require lower stimulation thresholds for evoking after-discharges when compared to WT mice.

Strengths:

(1) This study is well designed, and data support the conclusion that PRRT2 is a potent regulator of slow inactivation of Nav channels.

(2) This study reveals similar effects on Nav1.2 slow inactivation by PRRT2, TMEM233, and TRARG1, indicating a common regulation of Nav channels by DspB family members (Supplemental Figure 2). A recent study has shown that TMEM233 is essential for ExTxA (a plant toxin)-mediated inhibition on fast inactivation of Nav channels; and PRRT2 and TRARG1 could replicate this effect (Jami S, et al. Nat Commun 2023). It is possible that all three DspB members regulate Nav channel properties through the same mechanism, and exploring molecules that target PRRT2/TRARG1/TMEM233 might be a novel strategy for developing new treatments of DspB-related neurological diseases.

We thank the reviewer for careful evaluation and insightful suggestions.

Weaknesses:

(1) Previously, the authors have reported that PRRT2 reduces Nav1.2 current density and alters biophysical properties of both Nav1.2 and Nav1.6 channels, including enhanced steady-state inactivation, slower recovery, and stronger use-dependent inhibition (Lu B, et al. Cell Rep 2021, Fig 3 & S5). All those changes are expected to alter neuronal excitability and should be discussed.

We thank the reviewer for this suggestion. Although the present study focuses on PRRT2-dependent regulation of slow inactivation, we agree that PRRT2 may influence excitability through additional Nav-dependent mechanisms, including reduced current density and shifts in the voltage dependence of channel inactivation (Fruscione et al., 2018; Lu et al., 2021; Valente et al., 2023). Notably, because PRRT2 facilitates entry of Nav channels into slow-inactivated states both from closed states and from open states during prolonged depolarization, some of these previously reported effects may partly reflect enhanced slow inactivation and the resulting reduction in Nav channel availability. We have expanded the Discussion to integrate these prior findings and to clarify that these additional PRRT2-dependent effects may converge to shape neuronal excitability. Please refer to Page 16, Lines 445-452.

(2) In this study, the fast inactivation kinetics was examined by a single stimulus at 0 mV, which may not be sufficient for the conclusion. Inactivation kinetics at more voltage potentials should be added.

We thank the reviewer for this helpful suggestion. In the revised manuscript, we expanded our analysis of Nav1.2 fast-inactivation kinetics to include a range of test potentials (-20, -10, 0, +10, +20 and +30 mV) in the presence and absence of PRRT2. These experiments showed that PRRT2 expression did not significantly affect Nav1.2 fast-inactivation kinetics under these conditions. We have incorporated these new results into the revised manuscript. Please refer to Page 4, Lines 100-103; Figure 1C.

(3) It is a little surprising that there is no difference in Nav1.2 current density in axon-blebs between WT and Prrt2-mutant mice (Figure 7B). PRRT2 significantly shifts steady-state slow inactivation curve to hyperpolarizing direction, at -70 mV, nearly 70% of Nav1.2 channels are inactivated by slow inactivation in cells expressing PRRT2 when compared to less than 10% in cells expressing GFP (Figure supplement 1B); with a holding potential of -70 mV, I would expect that most of Nav channels are inactivated in axon-blebs from WT mice but not in axon-blebs from Prrt2-mutant mice, and therefore sodium current density should be different in Figure 7B, which was not. Any explanation?

We thank the reviewer for raising this point. In our axonal bleb recordings, although the holding potential was -70 mV, sodium current density was measured after a hyperpolarizing pre-pulse to -110 mV, which was applied before the test depolarization to relieve inactivation as much as possible (as described in the Methods). Therefore, the current density measurement in Figure 7B reflects the available current after this recovery step, rather than the steady-state availability at -70 mV. The lack of a difference in Figure 7B does not contradict the PRRT2-dependent shift in steady-state slow inactivation. In the revised manuscript, we have clarified this point explicitly in the Results and figure legend to avoid confusion. Please refer to Page 10, Lines 294-295.

(4) Besides Nav channels, PRRT2 has been shown to act on Cav2.1 channels as well as molecules involved in neurotransmitter release, which may also contribute to abnormal neuronal activity in Prrt2-mutant mice. These should be mentioned when discussing PRRT2's role in neuronal resilience.

We thank the reviewer for this suggestion. In addition to the Nav-dependent mechanisms, previous studies have shown that PRRT2 also regulates synaptic vesicle cycling (Valente et al., 2016; Coleman et al., 2018; Tan et al., 2018) and presynaptic surface expression of Cav2.1 channels (Ferrante et al., 2021). These effects are also expected to influence neurotransmitter release and, consequently, neuronal and network excitability. In the revised manuscript, we have expanded the Discussion to acknowledge that these additional PRRT2-dependent mechanisms may also contribute to cortical resilience. Please refer to Page 16, Lines 452-457.

Reviewer #3 (Public review):

This paper reveals that the neuronal protein PRRT2, previously known for its association with paroxysmal dyskinesia and infantile seizures, modulates the slow inactivation of voltage-gated sodium ion (Nav) channels, a gating process that limits excitability during prolonged activity. Using electrophysiology, molecular biology, and mouse models, the authors show that PRRT2 accelerates entry of Nav channels into the slow-inactivated state and slows their recovery, effectively dampening excessive excitability. The effect seems evolutionarily conserved, requires the C-terminal region of PRRT2, and is recapitulated in cortical neurons, where PRRT2 deficiency leads to hyper-responsiveness and reduced cortical resilience in vivo. These findings extend the functional repertoire of PRRT2, identifying it as a physiological brake on neuronal excitability. The work provides a mechanistic link between PRRT2 mutations and episodic neurological phenotypes.

We thank the reviewer for this positive evaluation of our work and for the constructive comments.

Comments:

(1) The precise structural interface and the molecular basis of gating modulation remain inferred rather than demonstrated.

We thank the reviewer for this comment. To avoid over-interpretation, we have removed the AlphaFold-based interaction prediction from the revised manuscript. We have also expanded the Limitations section to emphasize that direct structural and biochemical mapping of the PRRT2-Nav channel interface—through approaches such as targeted mutagenesis, crosslinking, and structural determination—will be required to define the binding interface and establish the molecular basis of gating modulation. Please refer to Page 16, Lines 465-468.

(2) The in vivo phenotype reflects a complex circuit outcome and does not isolate slow-inactivation defects per se.

We agree with the reviewer. Impaired slow inactivation in Prrt2-mutant mice is one plausible contributor to reduced cortical resilience. PRRT2 has also been reported to regulate surface exposure of Nav and Cav2.1 channels (Ferrante et al., 2021), as well as neuronal synaptic vesicle cycling (Valente et al., 2016; Coleman et al., 2018; Tan et al., 2018). Each of these PRRT2-associated processes could influence cortical excitability in vivo. We have therefore expanded the Discussion to clarify that the cortical phenotype likely reflects the combined contribution of multiple PRRT2-dependent mechanisms, rather than an isolated defect in slow inactivation alone. Please refer to Page 16, Lines 446-458.

(3) Expression of PRRT2 in muscle or heart is low, so the cross-isoform claims are likely of limited physiological significance.

We thank the review for this comment regarding physiological relevance. In the revised manuscript, we clarify that the cross-isoform analysis was intended to assess mechanistic generality at the channel level, rather than to imply equivalent physiological relevance across tissues. The functional consequence of PRRT2 depend on the Nav isoform composition and cellular context of each tissue. We also note that the broad isoform activity of the PRRT2 should be considered in any future attempt to manipulate PRRT2 function therapeutically. Please refer to Page 14 and 15, Lines 414-416; Lines 429-430.

(4) The mechanistic separation between the trafficking effect of PRRT2 and its gating effects is not clearly resolved.

We thank the reviewer’s concern regarding the possible contribution of trafficking effects to PRRT2-dependent regulation of Nav channel slow inactivation. Previous studies in heterologous overexpression systems have shown that PRRT2 can influence Nav channel trafficking and surface expression, raising the possibility that the observed effects on slow inactivation regulation might be secondary to altered channel abundance or localization. However, slow inactivation develops on a timescale of tens of milliseconds to seconds, whereas detectable changes in Nav channel trafficking and surface abundance generally occur over much longer intervals (minutes to hours) (Freal et al., 2023; Higerd-Rusli et al., 2023). These distinct temporal profiles argue against trafficking as the primary basis for the effects of PRRT2 on Nav channel slow inactivation described here, although direct quantification of dynamic changes in Nav channel surface expression will be required to fully exclude such a contribution (Liu et al., 2022; Tyagi et al., 2025). We have incorporated this point into the Discussion section. Please refer to Pages 13, Lines 378-388.

(5) Additional studies with Nav1.6 should be carried out.

We thank the reviewer for this suggestion. We have performed experiments to directly examine the effects of PRRT2 on Nav1.6 slow inactivation and incorporated these new data into the revised Results and figures, please refer to Page 8, Lines 211-215; Figures 4E and J.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Suggestions for future experiments (not for this paper)

(1) Exploit the lower protein expression in V5-PRRT2 mice to examine the effects of a hypomorphic allele.

We thank the reviewer for this insightful suggestion. We note that the V5 epitope knock-in reduced PRRT2 protein expression, which may functionally resemble a hypomorphic allele. Accordingly, in addition to its utility for biochemical experiments (e.g., co-immunoprecipitation), this line could serve as a genetic tool to interrogate PRRT2 dose-dependent effects in vivo. We have added this point to the revised manuscript, please refer to Page 9, Lines 265-267.

(2) Examine disease-causing PRRT2 mutations.

We thank the reviewer for this constructive suggestion. Testing disease-associated PRRT2 variants for their ability to regulate Nav channel slow inactivation would be an important next step to strengthen the disease relevance of the mechanism proposed here. Moreover, identifying missense variants that selectively disrupt slow-inactivation regulation could help pinpoint residues that are critical for PRRT2-Nav functional coupling and thereby inform future structure-function studies. We plan to pursue this direction in follow-up work.

(3) Investigate spreading depolarization in PRRT2-deficient mice.

We thank the reviewer for this suggestion. Although we have shown that PRRT2 deficiency facilitates spreading depolarization in the cerebellum, whether PRRT2 exerts similar control over spreading depolarization susceptibility in the cerebral cortex remains to be determined. We plan to address this in an independent study and to test how cortical spreading depolarization relates to other PRRT2-associated neurological disorders.

Reviewer #2 (Recommendations for the authors):

This study is, in general, well executed, and the manuscript is well written. However, I do have some questions.

(1) The authors' previous works have shown that PRRT2 regulates both Nav1.2 and Nav1.6, considering the wide expression Nav1.6 in CNS and its role in neuronal activity, what makes the authors not include Nav1.6 in this study?

We thank the reviewer for raising this question. In our previous work, PRRT2 produced broadly similar effects on Nav1.2 and Nav1.6. Therefore, in the initial version of this study, we focused primarily on Nav1.2 as a representative neuronal Nav channel isoform and placed greater emphasis on testing whether PRRT2-dependent regulation of slow inactivation extends across additional Nav isoforms. In response to reviewers’ concern, we have now performed new experiments to directly examine the effect of PRRT2 on Nav1.6 slow inactivation. These results have been incorporated into the revised manuscript. Please refer to Page 8, Lines 211-215; Figures 4E and J.

(2) Please explain why you chose 0 mV rather than -70 mV (closer to membrane potential) in the slow inactivation protocol.

We thank the reviewer for raising this question. Nav channels can enter into slow inactivation from both resting/closed states and activated/open states. In our steady-state slow-inactivation assays, we found that PRRT2 enhances Nav1.2 slow inactivation under both conditions (Figure 1-figure supplement 1A and B). In whole-cell recordings, Nav1.2 channels typically begin to activate at command voltages more depolarized than approximately -60 mV. Accordingly, a conditioning voltage of -70 mV predominantly probes entry into slow inactivation from closed states, whereas 0 mV drives channel activation and more effectively induces slow inactivation. We therefore chose 0 mV as the primary conditioning potential because it is widely used in conventional slow inactivation protocols and induces slow inactivation more robustly than conditioning voltages at -70 mV. We have added this explanation in Methods section of revised manuscript, please refer to Page 20, Lines 569-571.

(3) The authors mentioned that the insertion of V5 markedly reduced the PRRT2 protein level; thus, Prrt2-V5 knock-in mice could be considered as PRRT2 knock-down mice. Is there any noticeable difference in phenotype between Prrt2-V5 knock-in mouse and Prrt2-mutant mouse? In other words, is PRRT2 knockdown sufficient to affect neuronal excitability, or is a complete PRRT2 ablation required?

We thank the reviewer for raising this concern regarding the functional consequences of reduced PRRT2 expression in the Prrt2-V5 knock-in mice. Given that PRRT2 protein levels are markedly reduced in this line, and that cerebellar stimulation-induced dystonia is a characteristic phenotype of PRRT2 deficiency, we tested whether Prrt2-V5 knock-in mice also exhibit this phenotype. We found that electrical stimulation of the cerebellar cortex induced dystonia-like attacks in a subset of Prrt2-V5 knock-in mice. These dystonic behaviors resembled those previously observed in Prrt2-mutant mice, whereas no such behaviors were induced in wild-type mice (Figure 6-figure supplement 1). These findings indicate that a substantial reduction of PRRT2 expression (approximately 80%) is sufficient to impair neuronal function and elicit a disease-relevant phenotype in a subset of animals, supporting the interpretation that the V5 knock-in allele is hypomorphic. We have incorporated these results into the revised manuscript, please refer to Page 9, Lines 265-267; Figure 6-figure supplement 1.

(4) In Discussion (Page 13, lines 358-361), the authors mentioned a putative interaction between PRRT2 and the Nav channel by modeling, while there is no related data. Please either add modeling data or remove those sentences.

We thank the reviewer for this suggestion. To avoid over-interpretation, we have removed the statements regarding the AlphaFold-based interaction model from the revised manuscript. We agree that the interaction interface remains to be demonstrated experimentally, and we now discuss this point in the Limitations section. Please refer to Page 16, Lines 465-468.

(5) Typo: Page 14, line 399, "TMEM232" should be "TMEM233".

We thank the reviewer for pointing out this typo. We have corrected it in the revised manuscript.

Reviewer #3 (Recommendations for the authors):

(1) Mechanistic depth: While the functional data show altered slow-inactivation kinetics, the mechanistic explanation remains superficial. The AlphaFold-based prediction of PRRT2 interaction with DIV-S3 is speculative. The authors should clarify their illustrative rather than evidential intent and avoid over-interpretation.

We thank the reviewer for this comment. To avoid over-interpretation, we have removed the AlphaFold-based interaction prediction from the revised manuscript. We have also expanded the Limitations section to emphasize that direct structural and biochemical mapping of the PRRT2-Nav interface, including targeted mutagenesis, crosslinking, and structural determination, will be necessary to elucidate the molecular basis of this interaction and its effect on channel gating. Please refer to Page 16, Lines 465-468.

(2) Separation of trafficking vs. gating effects: Previous studies showed PRRT2 influences Nav trafficking and surface expression. Here, surface expression changes are not systematically quantified. Such an analysis would strengthen the argument that gating effects are not secondary to altered channel abundance or localization.

We thank the reviewer’s concern regarding the possible contribution of trafficking effects to PRRT2-dependent regulation of Nav channel slow inactivation. We agree that direct analysis of Nav channel surface localization during prolonged depolarization and hyperpolarization would provide stronger evidence to distinguish gating effects from trafficking-dependent mechanisms. However, such experiments are technically challenging in this context: conventional surface biotinylation assays do not provide the temporal resolution required for these rapid protocols, and live-cell imaging approaches to monitor dynamic changes in Nav channel surface expression during slow-inactivation paradigms have not yet been established in our laboratory.

Although PRRT2 has been reported to regulate Nav channel surface expression in heterologous systems, we consider it unlikely that trafficking is the major determinant of the slow-inactivation effects described here. Slow-inactivation develops on a timescale ranging from tens of milliseconds to seconds, whereas detectable changes in Nav channel trafficking and surface abundance generally occur over much longer timescales (minutes to hours) (Freal et al., 2023; Higerd-Rusli et al., 2023). We have expanded the Discussion in a revised manuscript. Please refer to Pages 13, Lines 378-388.

(3) Isoform generalization: Data on other Nav channel subtypes are presented as evidence of a conserved mechanism. However, given tissue-specific expression of PRRT2, these findings may be of limited in vivo relevance. At the very least, additional studies with Nav1.6 should be carried out.

We thank the review for this suggestion. In response, we conducted new experiments to examine the effect of PRRT2 on Nav1.6 slow inactivation. These results show that PRRT2 promotes entry of Nav1.6 channels into slow-inactivated states and delays their recovery, consistent with its effects on the other Nav isoforms examined in this study. We have incorporated these new data into the revised manuscript. Please refer to Page 8, Lines 211-215; Figures 4E and J.

Furthermore, we clarify that the cross-isoform analysis was intended to assess mechanistic generality at the channel level, rather than to imply equivalent physiological relevance across tissues. The functional consequence of PRRT2 depend on the Nav isoform composition and cellular context of each tissue. We also note that the broad isoform activity of the PRRT2 should be considered in any future attempt to manipulate PRRT2 function therapeutically. Pages 14 and 15, Lines 414-416 and 429-430.

(4) In vivo functional link: The EEG after-discharge threshold assay suggests decreased cortical resilience, but causality between slow-inactivation impairment and hyperexcitability remains indirect. Complementary in vivo recordings would strengthen the physiological link.

We thank the reviewer for this helpful suggestion. To further link impaired slow-inactivation to the hyperexcitability, we applied a repetitive stimulation protocol in corpus callosum slices, a white-matter region of brain enriched in both PRRT2 and Nav channels. During high-frequency stimulation (e.g., 20 Hz), the amplitude of the compound action potential progressively decreased over the course of the stimulus train. This phenomenon, often referred to as adaptation, reflects activity-dependent reduction in Nav channel availability (Fleidervish et al., 1996; Mickus et al., 1999; Kim et al., 2012). Compared with wild-type mice, Prrt2-mutant mice exhibited less adaptation during high-frequency stimulation, consistent with impaired slow inactivation during repetitive activity, which may contribute to hyperexcitability (Figure 7-figure supplement 2). We have added these results to the revised manuscript. Please refer to Pages 11, Lines 311-322; Figure 7-figure supplement 2.

(5) Structural interaction: It remains unclear whether PRRT2 binds the α-subunit directly or through accessory proteins. Crosslinking or detergent-solubilization controls of different stringencies could clarify this.

We thank the reviewer for raising this important issue. We agree that our co-immunoprecipitation data do not distinguish whether PRRT2 associates with the Nav channel α-subunit directly or through other components of the protein complex. To avoid over-interpretation, we have revised the relevant text in the manuscript to remove any implication of direct binding and now describe the result as an association between PRRT2 and Nav channels.

We have also expanded the Limitations section to note that additional experiments, such as crosslinking and structural studies, will be required to define the interaction interface between PRRT2 and Nav channels. Please refer to Page 16, Lines 465-468.

(6) Comparisons to other regulators: The paper positions PRRT2 as distinct from FHFs and β-subunits. The data support this, but the discussion could more critically assess whether PRRT2 acts by stabilizing a pore-based inactivated conformation, as suggested for other slow-inactivation modulators.

We thank the reviewer for this insightful suggestion. At present, relatively few modulators have been characterized in detail with respect to their effects on Nav channel slow-inactivation kinetics. Moreover, even for compounds such as lacosamide, which has been proposed to act as a slow-inactivation modulator, the underlying mechanism remains under debate (Errington et al., 2008; Jo and Bean, 2017). Therefore, in the revised manuscript, we discussed the possible mechanism of PRRT2 in the context of current models of Nav channel slow inactivation.

Previous studies suggest that entry into the slow-inactivated state involves at least two coupled processes: conformational changes in the voltage-sensing domains and structural rearrangements in the pore region, including the selectivity filter and intracellular activation gate (Catterall et al., 2024; Silva, 2014). During prolonged depolarization, voltage sensors become stabilized in the up-state, while the pore undergoes progressive rearrangements associated with slow inactivation (Balser et al., 1996; Vilin et al., 1999). Thus, mechanisms that further stabilize voltage sensors in the up-state and/or facilitate pore-based inactivated conformations could enhance slow inactivation.

Within this framework, PRRT2 may enhance slow inactivation by facilitating one or both of these processes, although direct evidence is still lacking. We have incorporated this discussion in relative section of revised manuscript. Please refer to Page 14, Lines 389-404.

Response references:

Jo S, Bean BP. Lacosamide Inhibition of Nav1.7 Voltage-Gated Sodium Channels: Slow Binding to Fast-Inactivated States. Mol Pharmacol. 2017 Apr;91(4):277-286.

Errington AC, Stöhr T, Heers C, Lees G. The investigational anticonvulsant lacosamide selectively enhances slow inactivation of voltage-gated sodium channels. Mol Pharmacol. 2008 Jan;73(1):157-69.

(7) Behavioral/clinical link: Given the strong human genetics background of PRRT2 disorders, a brief analysis or reference to electrophysiological phenotypes in patient neurons would contextualize the cortical findings.

We thank the reviewer for this suggestion. Previous studies showed that iPSC-derived excitatory neurons from a patient carrying a homozygous PRRT2 mutation exhibited increased sodium currents and neuronal hyperexcitability (Fruscione et al., 2018). Given that slow inactivation regulates Nav channel availability and thereby influences neuronal excitability, these electrophysiological abnormalities in patient-derived neurons may, at least in part, reflect impaired PRRT2-dependent regulation of Nav channel slow inactivation. We have added this point to the relative section of the revised manuscript. Please refer to Pages 15, Lines 432-437.

Minor comments

(1) Figures should include statistical sample sizes (n) and ideally overlay data points rather than only means {plus minus} SEM.

We thank the reviewer for this suggestion. In the revised manuscript, we present both individual data points and mean ± SEM in the column graphs. For the line graphs, individual data points were not overlaid because of space and readability constraints, and these panels therefore display mean ± SEM only. Sample sizes for each group are provided in the corresponding figure legends.

(2) The AlphaFold model should be provided as a supplementary figure with confidence scores indicated.

We thank the reviewer for this suggestion. However, because the predicted Nav1.2-PRRT2 interaction interface has not yet been experimentally validated in our study, we chose to remove the AlphaFold-based model from the revised manuscript to avoid over-interpretation.

(3) Clarify whether TTX sensitivity was verified in the axonal bleb preparation.

We thank the reviewer for raising this point. We verified the identity of the sodium currents in the axonal bleb preparation by their sensitivity to TTX, and this information has now been added to Figure 7A in the revised manuscript. Please refer to Page 10, Line 290; Figure 7A.

and converting.

Headers are not optional,remove this bit. we had alresdy reeritten these. but dont ha e time to look for it unless you know where to look and what to replqce

replace with truly engages

email & deliverability help only a human can give. Free

this is just for /emailalmanac pages and under that. nowhere else

The form box is broken again etc: we had added two check boxes maybe you find the form design somewhere so it looks the same with the correct text and checkbox intelligence where the newsletter checkbox needs to be checked but the open rate one doesn't in order to submit the formSeems to be an issue other pages wherever there's the newsletter box

same point as the menu button, should go to https://preview.reviewmyemails.com/get-started

link to sos page

link to about page or my linkedin.com/in/yannatorry

We need to add all four contests that we want Litmus Coach Award 2020 MARsum Top 100 Marketing & Advertising Leaders 2021 Marketing 2.0 Best Business Award 2022 Lenovo Twinning Finalist 2026 Ask a Deliverability Expert·8,000+ lists reviewed

Put them in whatever order makes more sense, unless we purposofully decided to show this only.

https://reviewmyemails.com/get-started until we publish app.reviewmyemails.com might want to rename this menu button for now too?

Please delete this page and replace with a simple "complete" message.

1. Same comments on page structure as for #1. It would be great to have the feedback closer to the answers.
2. Could the words be more aligned - ex., Spoke/Hablabamos or walked/caminábamos
3. Here also, I think there needs to be something to contextualize the words at the top - ex., "Read each example and compare the grammatical information encoded within each word."

Same overall comments on page structure. Could we also have a call to action here? ex. "Select the claims about human analysis and/or NLP output that are accurate."

Delete the first sentence.

Same feedback here about the page structure. Please also move the call to action - "Choose the parse that matches the intended meaning" - over the right, next to the choices.

Please convert these to steps (with some text edits): 1. Drag or tap the correct morpheme tiles into the structure slots. 2. Select a feature chip to add it to the final feature set. (Select the chip again to remove it from the feature set.)

It's a little confusing that the feedback shows up here rather than next to the final feature set.

Could we divide this section to align with the instructions. Maybe "1. Morphemes" (include the morpheme tiles and the structure slots. Then "2. Feature Set", including the Feature Chips and Final Feature Set.

This seems like the place for the "Guided Warm-Up" banner. "Practice 1" seems redundant, adding new but unnecessary terminology.

Because the bolded text is blue, it reads as a hyperlink. Maybe just black, or a different color?

Maybe this is not necessary? The four bolded titles for the four different pages are more important..

Maybe "This lab includes four short practice pages..."

This was a bit confusing for me because it looked like something I was supposed to interact with, but it seems it's just there for the visual. Since the activities are listed on the left, can we delete these blocks and just let this be a more simple overview?

ok so for this link for now and all other app.reviewmyemails.com we need to redirect to https://preview.reviewmyemails.com/website/get-started

eLife Assessment

This important study investigates how surface stickiness shapes whisker mechanics and peripheral neural responses during active touch. The biomechanical evidence that surface stickiness alters whisker mechanics and stick-slip dynamics is compelling, supported by a large and high-quality 3D dataset, while the electrophysiological evidence is solid but limited by a small sample size and insufficient validation of the sticky stimuli. The work will be of broad interest to sensory neuroscientists studying active touch.

Reviewer #1 (Public review):

Summary:

This study offers a careful and technically strong look at how surface stickiness changes whisker-surface interactions and how that information reaches peripheral sensory neurons. The authors use 3D whisker tracking to capture bending, twisting, rolling, and tip motion during contact with surfaces that differ in stickiness, coarseness, and position. They show that sticky surfaces, especially silicone, broaden the range of whisker deformation, produce stronger but less frequent stick-slip events, and change firing rates in some trigeminal ganglion neurons. Overall, the study is valuable because it goes beyond standard 2D tracking and shows that out-of-plane motion and roll are important for understanding how whiskers encode texture.

Strengths:

The study is technically strong and well motivated. Its main strength is the use of 3D whisker tracking to show that surface stickiness affects whisker deformation in ways that standard 2D tracking would miss, including torsion, roll, out-of-plane motion, and stick-slip dynamics. The authors also connect these mechanical effects to TG activity, providing evidence that stickiness information is available in peripheral sensory responses. Overall, the work expands the study of whisker-based texture sensing beyond coarseness and provides a richer biomechanical framework for understanding tactile encoding.

Weaknesses:

The main weakness is that stickiness is not formally defined early in the manuscript, even though it is the central experimental variable. Several methodological choices also need clearer justification or validation, including the use of 2D measures as comparators for torsion and roll, the thresholds used for stick-slip detection, the degree-5 polynomial fit, the reference ROI, and aspects of the 3D surface reconstruction. The neural evidence should also be interpreted cautiously because the TG sample is small, only a subset of units discriminated silicone, and the correlation between strain sensitivity and silicone discrimination is suggestive rather than definitive.

Reviewer #2 (Public review):

The authors explore the sensation of stickiness from the point of view of whisker exploration and encoding in the trigeminal ganglion. In doing so, they develop methods for 3D whisker tracking to describe stick-specific parameters such as stick-slip rates and strain. Overall, the methods are strong, and the authors present the results appropriately. Overall, I think exploration of the sensation of stickiness is a great question.

My main criticism is in relation to the chosen stimuli, and I wonder whether the authors may have room to explore more naturally sticky materials and what this may mean for the animal.

(1) Chosen stimuli for stickiness:

Four different materials are used, with the aim of presenting animals with graded measures of stickiness. The results show that silicone stands out against the others; it's less clear whether the intermediate textures (Delrin and resin) may be truly intermediate in stickiness.

I wonder if the stimuli chosen were truly representative of the aim of providing a gradient of stickiness. Did the materials differ in other features, such as surface temperature, texture, etc., which could explain some results? The authors discuss this in terms of coefficients of friction and how these estimates are not quantified in relation to whiskers themselves.

Measures of stick-slip and strain with silicone vs other materials make intuitive sense. Could the authors add additional naturally sticky stimuli to exemplify the results? For example, adhesive, glue, or a sugary substance.

(2) Tracking methods and quantification:

The 3D tracking methods, which incorporate whisker twists, strain, and other fine features of whisker exploration, present an advance in terms of analysis of how whiskers may explore more complex, natural features of environments. The analyses and quantifications are all solid and robust. The technical approaches are well-prepared to take the work a step further in terms of stimulus choice.

(3) Peripheral coding of stickiness:

The authors report that some units respond preferentially to whisking on silicone and that this has to do with strain on the whisker. Is there a possibility to understand the nature or anatomy of these units and why they might be preferential for the sticky sensation? Can the location in the follicle be assigned? And/or would the methodology allow for assignment of where the specifically sticky-tuned units project centrally?

(4) Relationship to natural stimuli:

A piece missing from the paper is more discussion and exploration of why stickiness may be important for sensory coding, as well as potentially more naturally sticky stimuli. One could imagine that a mouse navigating the world could find stickiness attractive, if it were a source of sweet food, for example, or it could potentially be a sensation the animal prefers to avoid. Stickiness could also indicate contamination or a sticky trap, to be avoided. If the authors are able to add naturally sticky stimuli, the whisker exploration and encoding could potentially provide further cues towards the valence of stickiness for mice.

Reviewer #3 (Public review):

This paper tackles an underexplored dimension of whisker-based texture sensing: while surface coarseness encoding has been extensively characterized in rodents, the mechanical and neural basis for stickiness sensing has not previously been examined. The authors make two intertwined contributions that together represent a substantial advance: a methodological one - a 3D whisker tracking pipeline operating at 4000 fps, capable of capturing torsion, roll, and out-of-plane whisker motion - and a scientific one - a first characterization of how whisker mechanics and primary trigeminal afferent responses differ between surfaces of high and low stickiness. The work is technically solid, the dataset is large, and the question is well motivated both by the multidimensional nature of tactile texture perception and by the practical advantages of the whisker system for studying touch mechanics.

Strengths.:

The 3D tracking system is a timely advance over existing tools, particularly in its handling of non-planar whisker shapes and the full automation required for the sub-millisecond resolution needed to detect stick-slip events. The mechanical dataset is extensive. The finding that whisking against silicone expands the sampled whisker strain space and produces stronger but less frequent stick-slip events is clearly demonstrated and internally consistent with the proposed mechanism of greater strain accumulation before frictional release - a physically intuitive result. The open release of the tracking code considerably increases the value of this work to the broader community.

Weaknesses:

A few aspects of the paper, if sharpened, would considerably strengthen the evidence and the clarity of the conclusions.

The central claim - that "stickiness information is available to the whisker system" - does not capture the precision of what the paper demonstrates. As stated, the finding is close to guaranteed: any variation in surface friction will produce some change in whisker mechanics, so the presence of mechanical differences between materials is expected rather than surprising. The more valuable question the paper is well positioned to answer is which specific dimensions of the whisker mechanical response are most informative about surface stickiness. The paper reports effects on strain distribution breadth, stick-slip amplitude, and stick-slip rate, but does not synthesize which of these - or which sub-dimensions (bending, twisting, or rolling) - carry the most discriminating information. Identifying the salient dimensions of the mechanical response and relating them to the proposed frictional mechanism would sharpen the paper's conclusions substantially.

A related but distinct limitation is the absence of direct force measurements during whisker-surface contact. The authors acknowledge this openly, and I recognize it is not easily remedied within the current experimental setup. It does, however, constrain interpretation: without knowing the actual forces generated at the whisker-surface interface, the assumed stickiness ordering of the tested materials cannot be validated, and - importantly - the relative contribution of surface friction and material compliance to the observed mechanical differences cannot be determined. This is an important direction for future work in this area.

The paper argues carefully that 2D tracking is insufficient for capturing the full mechanical picture of whisker-surface interactions, and the figure currently in the supplementary material (Figure S2) makes this case convincingly through multiple analyses. This argument is the core justification for the paper's methodological contribution and deserves a place in the main manuscript. Furthermore, while the mechanical case for 3D over 2D tracking is well made, it has not yet been tested at the neural level: the regression model used to predict neural firing incorporates 3D variables, but its performance is not compared against an equivalent model restricted to 2D variables. Such a comparison would directly demonstrate whether torsion and roll - the signals inaccessible to 2D tracking - carry neural predictive value, and would elegantly unite the paper's methodological and scientific contributions.

Finally, the three-dimensional plots in Figure 3 are the paper's primary representation of its main mechanical result, and there is a real opportunity to make them considerably more informative. The whisker deformation probability distributions (panel B) are rendered in 3D from a single viewing angle, making it difficult to assess the shape or anisotropy of the distributions - and in particular to see which dimensions expand most for silicone relative to the other materials. This is precisely the information needed to identify the most salient dimensions of the stickiness signal, and two-dimensional representations would make it directly readable.

Equipara-se a operações de crédito:

this is my annotation

why tho?

Algorithm Words (). Together.

What You see I see

https://www.instagram.com/reels/Daht3lDs3zo/

🟢Converse (). Related to emergence, explore, refinement, process.

🟢What if the algorithm can talk and take you everywhere realistically.

https://www.instagram.com/reels/DafuBHph6e-/

Sticky (). Using Memory and not being. New continuously. https://www.instagram.com/reels/DaOwpQdRfPe/

Variation (). World is full of things.

https://www.instagram.com/reels/DZEiMO8SDDt/

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Reply to the reviewers

Reviewer #1

1. The inverse relationship between PGCLC and DE efficiency is intriguing but under-explored. The observation that lines efficient for PGCLCs (Podx1, Kolf2) are poor at DE differentiation, and vice versa, is one of the key findings. Yet this is presented almost in passing. It would strengthen the paper considerably if the authors discussed whether their Polycomb-regulated gene set predicts DE efficiency with an inverse sign, and whether the logistic regression model can be tested on the DE data directly.

As suggested by the reviewer, we have enhanced our analysis of definitive endoderm (DE) differentiation efficiency and discussed it more prominently in the manuscript in the section “A subset of Polycomb targets is predictive of differentiation properties”. In particular, we now examine the correlation between gene expression from RNAseq and DE differentiation. For this purpose, we took the genes used to predict PGCLC efficiency (all of which are regulated by H3K27me3), and examined the correlation between their expression and the efficiencies of PGCLC and DE differentiation. We found that that differentiation is not a binary outcome for DEs, with many intermediate cases observed. Thus, instead of using a logistic regression, we employed a sigmoidal regression scheme for DEs. For this analysis, we used the absolute difference between observed and predicted efficiency, resulting in a mean absolute error of 12% [95% CI: 8-22%].

We have now included an extra panel (Figure 7C) showing the correlations between expression of the H3K27me3 genes and the differentiation efficiencies in both PGCLC and DE fates. As anticipated by the reviewer, this plot reveals an inverse correlation, which we highlight in the main manuscript. Further, we now mention that these genes can also be used to predict DE differentiation efficiency, with satisfactory accuracy (although the confidence interval is wide due to the small sample size, as in the case of PGCLCs).

The mathematical model is elegant but the choice to vary parameter E across cell lines needs stronger justification. The model assumes that inter-line differences are driven by variation in the overall rate of H3K27 methylation (parameter E). This is a reasonable starting assumption, but the authors should discuss alternative scenarios more explicitly. Could variation in demethylase activity, PRC2 recruitment strength, or replication timing equally well explain the data? The fact that EPOP is differentially expressed is mentioned as a potential mechanistic candidate for modulating E, which is compelling, but the link remains correlative. The authors should be more cautious in their language here, stating that EPOP "may be sufficient to completely switch the transcriptional regulation" goes beyond what the data show.

We thank the Reviewer for these suggestions and have tightened our discussion of these points. It is correct that variation in demethylase activity can also explain our data. We now explicitly point this out in the section “The behaviour of H3K27me3 can be explained using a simple mathematical model”. However, this possibility does not fit as well with the RNA expression data. While we found differential expression of the PcG gene EPOP, we did not detect any differential expression of the KDM6 histone demethylases (KDM6A-KDM6C). Therefore, we still favour our original suggestion of variation in the methylation rates over this possibility. In addition, we have moderated our wording on EPOP, stating in the Discussion that changes in EPOP expression “may be sufficient to alter the transcriptional regulation of specific target genes.”

The predictive model for differentiation efficiency is promising but the PGCLC training set is too small for confident generalisation claims.The authors acknowledge this (109 features, ~21 data points), and the L2 regularisation is appropriate. However, the claim of 91% accuracy with a 95% CI of 78-100% on the PGCLC data should be presented more cautiously. With such a small dataset, the confidence interval is very wide. The more convincing validation comes from the DN data (143 lines from Jerber et al.), where the model trained on PGCLC data performs comparably to the full-transcriptome model. This cross-fate generalisation is quite strong and should be emphasised more prominently as the primary evidence for the validity of the model.

We have followed the reviewer’s guidance and revised our language, when discussing the PGCLC case in the section “A subset of Polycomb targets is predictive of differentiation properties”. We have also emphasised more clearly the successful validation of the DN data.

1. The claim of "epigenetic memory" during differentiation (iPSC to pre-ME) is suggestive but would benefit from additional analysis. The authors show that 60% of pre-ME DEGs overlap with iPSC DEGs, and that H3K27me3-cluster genes maintain their expression patterns. However, 60% overlap could partly reflect genes that are simply not regulated during the short 12-hour pre-ME induction. To strengthen this claim, the authors should compare the overlap rate for H3K27me3-cluster genes specifically versus other clusters. If Polycomb targets show significantly higher overlap than, for example, K4&ATAC genes, this would more convincingly support a Polycomb-specific memory mechanism.

We have now performed this analysis, examining the persistence of DEGs into the pre-ME state (i.e., whether a gene that was differentially expressed in hiPSCs remains differentially expressed in pre-ME). Excluding the H3K9me3 cluster (the smallest cluster containing fewer than 25 genes), the K27 cluster is the most persistent cluster in terms of fraction of genes per cluster. When the clusters were pooled into the different variables involved (ignoring H3K9me3), H3K27me3 again emerged as the most persistent chromatin feature. Unfortunately, however, these results were not statistically significant, so we are unable to include them in the manuscript.

Lack of genetic background analysis. Ten lines from nine donors will harbour substantial genetic variation. The authors note that genetic variation has been linked to iPSC heterogeneity but do not analyse whether the three "outlier" lines (Kucg2, Sojd3, Yoch6) share genetic features. For instance, common variants at PRC2 component loci, EPOP regulatory variants, or structural variants that might alter H3K27me3 domain boundaries. The HipSci consortium provides genotyping data for these lines. A targeted analysis of variants at Polycomb-related loci would be feasible and could either strengthen the epigenetic interpretation or reveal a genetic confounder.

We thank the Reviewer for raising this important point. To investigate potential confounding effects due to genetic variation between the hiPSC lines in our panel, we performed a targeted analysis of genetic variation across Polycomb-related loci (H3K27me3 occupied loci and Polycomb group genes) in all ten cell lines (using whole genome sequencing data from the HipSci consortium). This analysis specifically tested whether the three “compromised” lines (Yoch6, Sojd3 and Kucg2) share consistent genetic variants relative to the seven “normal” lines. We identified 15 indels (out of 4115) that satisfied this criterium. However, all are located in non-coding regions and none overlap with ATAC-seq peaks. Hence, they are unlikely to function as gene regulatory elements (e.g., enhancers), but we cannot exclude the possibility that they affect gene expression in other ways. We have added a new Results section “Genetic variants shared between differentiation-compromised hiPSC lines” to discuss these points, as well as adding new text to the Discussion and Methods.

Minor Comments

The promoter definition ({plus minus}1 kb from gene start) is non-standard; most studies use a window upstream of the TSS rather than gene start. The authors mention they confirmed robustness to an alternative definition (-1 kb to gene start) but do not show this data. It should be included in the supplement.

We now show the data for the alternative promoter definition in Supplementary Fig. 5B and Supplementary Fig. 7C. These results demonstrate that our conclusions are robust to different promoter definitions.

For CUT&Tag, no spike-in normalisation is mentioned. Given that the key conclusions is based on quantitative comparisons of H3K27me3 levels across cell lines, the absence of spike-in controls is a potential concern. The authors should discuss whether technical variation between CUT&Tag libraries could contribute to the observed bimodality. At minimum, the correlation between replicates for H3K27me3 should be shown (presumably it is high, but this should be documented).

We thank the Reviewer for this suggestion. As now shown in Supplementary Fig. 4C, the correlation between our H3K27me3 replicates is indeed high (R between 0.93 and 0.96). Hence, technical variation between CUT&Tag libraries is unlikely to contribute to the observed bimodality.

The statistical test for the PGCLC/H3K27me3 overlap (p We thank the reviewer for noticing this. Indeed, this is the case. The test assumes independence of lines, which is in general a reasonable assumption, but may not always hold. Specifically, the Kolf2 and Kolf3 lines are derived from the same donor, which implies they are not completely independent. However, for all other lines, we still think independence is a reasonable assumption and, thus, the overall result of the test should be a good approximation. We have added this caveat to the manuscript.

Figure 6A: the heatmaps for H3K4, ATAC and H3K27 are shown side by side but at apparently different scales; this should be clarified or made consistent.

Indeed, the scales in all heatmaps are the same. We have clarified this in the captions of the figures.

Reviewer #2

1.) Figure 2B. Are all GO terms shown in the figure or are these just the top terms? If this is a suset then all terms should be provided as a supplemental table. If this is all significant terms, this is relitavely modest considering the number of DEGs (712) and is probably due to the fact that DEGs are derived from all comparisons and so could be diluted by the presence of multiple opposing effects. If this is the case, you could identify DEGs that define the PCA groupings and then re-run the GO analysis to potentially provide a better definition of the functional differences between groups of cell lines.

The GO terms previously displayed were the top hits. We have now included all the significant terms in Supplementary Files 4 and 5 (for the Molecular Function and the Biological Process ontologies, respectively).

Chromatin accessibility at gene promoters is a poor predictor of transcription, but it is likely that accessibility at distal regions (e.g putative enhancers) might be a better predictor. Did the authors look at this? This possibility should at least be mentioned when discussing the ATC-seq data and the lack of correlation with transcription.

• *

We thank the reviewer for this suggestion. To locate additional regulatory regions, we downloaded tracks for the enhancer-associated marks H3K4me1 and H3K27ac for the ten cell lines from Todd and colleagues (Todd et al., Genome Biology, 2025; https://genomebiology.biomedcentral.com/articles/10.1186/s13059-025-03658-8). We then intersected the ATAC-seq peaks with the H3K4me1 peaks in each cell line to identify putative enhancers. For each protein coding gene, we then identified the closest ATAC and H3K4me1 positive peak (among all cell lines), which we assumed was the most likely enhancer for that gene. We then evaluated the ATAC, H3K27ac and H3K27me3 signal within these enhancers for each cell line. With this information, we tried using a version of our SVM-based pipeline to improve our understanding of transcriptional regulation in genes within the ‘origin’ cluster (for which we failed to get significant insights from our standard SVM approach). Thus, we used seven variables as an input for the SVM: The four of the standard approach and three additional variables from the ATAC/H3K27ac/H3K27me3 signal at the nearest enhancer. However, for genes with an enhancer closer than 100kb, the performance of the SVM with enhancer variables was similar to the standard SVM (or slightly worse). If we focused on genes with enhancers 10kb or closer to the TSS (75 genes), then the SVM with the enhancer signal did modestly improve the prediction. However, when analysing the results more closely, it was only for a handful of genes (around 10) where the usage of the enhancer data was beneficial, and, even then, it was mostly down to the H3K27me3 signal rather than the more standard enhancer marks, such as H3K27ac or chromatin accessibility. This lack of improvement in the accuracy is probably due to our inability to identify the correct enhancers, as distance on the linear genome scale is often a poor predictor of enhancer-promoter interactions.

Ultimately, because the improvement is for such a small number of genes, we have not included this analysis in the manuscript. However, we do now mention in the manuscript in section “Chromatin accessibility does not always correlate with transcription” that we tried to include distal enhancers but that this approach was not successful.

2.) Fig 1C. Statistic overview at end of legend should be moved under section describing panel C in the legend.

We have now made this change.

3.) 'Furthermore, the transition value of 30% enables repression to be stably maintained even after DNA replication, when, on average, histone modification levels will be transiently halved'. Whilst this is potentially true and a plausible interpretation, you cannot exclude that the signal is not derived from different cell populations in the culture due to cellular heterogeneity such as cell cycle or spontaneous differentiation. This possibility should be noted in the text.

We thank the Reviewer for this suggestion. Due to the possible alternative explanations pointed out by the reviewer, and to minimise any possible misunderstandings, we decided to drop this sentence from the manuscript, which is not required for any of our main conclusions.

4.) 'Higher values indicate stronger correlation or anticorrelation and, thus, stronger differences between cell lines.' I don't believe this makes sense as written. Do the authors mean stronger partitioning of different iPSC lines into clusters?

Indeed, this sentence wasn’t very clear -- we have now rewritten it to improve clarity: “Because absolute correlation values were used, high values indicate that expression profiles between two cell lines are either highly correlated or highly anticorrelated. Across all pairwise comparisons, high values suggest strong partitioning of cell lines with highly similar or markedly different transcriptional profiles.”

5.) 'We found that 60% of the DEGs in pre-ME were also DEGs in hiPSCs'. This needs to be made clearer. Do the authors mean DEGs between iPSCs following differentiation or DEGs between undifferentiated iPSCs and their differentiated derivatives? The former suggests that the iPSCs are already partially differentiated and that differentiation in promoted or constrained by this starting state whilst the latter would suggest that some lines are skewed towards the mesendoderm.

We mean that of the genes that are differentially expressed between the 10 lines in pre-ME, 60% were also differentially expressed between the 10 lines in iPSCs (prior to differentiation). We have reworded this sentence to make it clearer.

6.) 'Finally, histone marks in the iPSC state were also predictive of expression in the pre-ME state, albeit with slightly lower accuracy than for the iPSC state (Supplementary Fig. 8C, D), which may indicate the existence of an epigenetic memory system that is maintained during differentiation.' Or the retention of an epigenetic signature that failed to be erased during the initial generation of the iPSCs.

We agree with the reviewer that this is entirely possible: our point is that memory states may persist from iPSCs to pre-ME. The memory state may of course predate the initial generation of the iPSCs. We have amended the section “Pre-ME transcriptomes suggest inheritance along the developmental trajectory” to include this possibility.

7.) 'To minimise the risk of overfitting, only reliable targets were retained'. Whilst this is outlined in the methods as stated, a summary of what this means should be included in the body text.

We thank the Reviewer for this suggestion. We have included the required extra text in the section “A subset of Polycomb targets is predictive of differentiation properties”. We have also revised the performance metrics so that they are strictly comparable with the results of Jerber and colleagues (which implies, in some cases, removing error bars, as in the results of Jerber et al., 2021). The reviewer may notice differences in the values reported but all our claims remain valid.

Reviewer #3

The major claim that among histone modifications that have been profiled in this manuscript, H3K27me3 is the most predictive for expression is supported by the analysis. However the analysis may be skewed because the RNAseq and the H3K27me3 difference are driven by the extreme skewing of the 3 cell lines Yoch6, Sojd3 and Kucg (Fig 2A, 2C and 6A). Two of these lines cannot form EBs at all, a major failure in their pluripotent characteristics.

We thank the reviewer for raising this fundamental point. Our aim for this study was to use iPSC lines that have passed existing standards and could easily be chosen from a panel of lines by an unsuspecting user. Indeed, the differentiation-compromised lines in our study are indistinguishable from other PSCs from a validated source that extensively characterises the distributed material (HipSci resource, https://www.hipsci.org). This source categorises these cell lines as correctly reprogrammed and fully pluripotent. In addition, we now present PluriTest data (doi: 10.1038/nmeth.1580) from all normal lines available from the HipSci resource (835 lines) and highlight the ten cell lines used in this study (see Supplementary Fig. 1A). All cell lines in our panel have pluripotency scores over 20, and all but one (Bima1 – which notably differentiates efficiently into PGCLCs and DNs) have novelty scores below 1.67; these values have been empirically determined as pluripotency signature thresholds (Müller et al., 2011). This analysis clearly demonstrates that the cell lines in our study are not outliers, an important fact which we have now added to section “Marked differences in the developmental efficiency of hiPSC lines”.

Furthermore, one of the key advances of our study is that we identify a chromatin and transcription signature that will enable researchers in the stem cell community to identify iPSC lines with compromised differentiation potential early on. We also note that compromised differentiation potential is widespread among human PSCs. For example, Jerber et al. report that 48 out of 183 hiPSC lines could not be differentiated successfully into dopaminergic neurons (doi:10.1038/s41588-021-00801-6). Thus, our study addresses an important and widespread issue in the stem cell field, a point we now emphasise in the introduction of the manuscript.

Further, one of the lines that can form EBs, fails to make PGCLCs but can differentiate into DE, Letw5 has neither the RNA profile nor the H3K27me3 profile of the skewed iPSC lines. Therefore, whether H3K27me3 truly influences phenotype at least in terms of PGCLC and DE differentiation of iPSCs is not supported by the analysis in the manuscript.

We agree that the behaviour of Letw5 is interesting, and we discuss its properties extensively in section “Marked differences in the developmental efficiency of hiPSC lines” and Fig. 1E. As we state, comparing Letw5 with Kucg2, “These findings suggest that Kucg2 hiPSCs have limited developmental competence to generate PGCLCs, while Letw5 hiPSCs are capable of PGCLC specification but fail to sustain the germ cell fate, pointing to a defect in fate maintenance rather than in initial developmental capacity.” Hence, the evidence points towards Letw5 having a separate defect which is unrelated to the impaired Polycomb regulation identified in the other three problematic lines. We also emphasise this point in section " A major role for H3K27me3 in hiPSC transcriptional heterogeneity", where we state that "[...] in this case [Letw5], a distinct mechanism, independent of H3K27me3 dysregulation, may result in impaired germ cell development."

1. What are the predictions from applying SVM to data from only the 6 cell lines Podx, Kolf2, Kolf3, Bima 1, Qolg1, Wibj2. The DE differentiation potential will also have to be measured for each of these cell lines.

Following the reviewer’s suggestion, we applied the SVM only to data from those six cell lines (which do not include any of the defective cell lines), see section “Linking variation in chromatin features with transcriptional output using SVMs”. Given that the SVM only takes as input data from differentially expressed genes, the set of genes used decreased markedly as there are fewer genes differentially expressed among these cell lines (125 DEGs). Nevertheless, for this subset of genes, the SVM still retains satisfactory accuracy (both AUROC and overall accuracy in the 70% to 75% range; now shown in Supplementary Fig. 6H). This result is particularly remarkable given that the SVM is operating with very little data (five datapoints for training and one for testing, per gene) and that the cell lines are very similar to each other. As the reviewer points out, we hope these results might encourage other researchers to pursue similar analysis approaches.

For DE differentiation, we previously included data (Supplementary Fig. 3B, C) for the following lines: Podx1, Kolf2, Kucg2, Letw5, Sojd3, and Yoch6. Only Kolf3, Bima1, Qolg1 and Wibj2 were missing. We have also now measured DE differentiation in three remaining lines (Kolf3, Qolg1, and Wibj2).

The above analysis may also shed light on howextreme the input parameters must be for SVM to be a good classifier? Such an analysis may also assist future users of the method to assess whether SVM would be useful for their datasets.

Please see our previous answer. We argue that the results presented above for six similar cell lines imply that this type of computational approach can have general applicability and does not require extreme inputs. We have followed the Reviewer’s suggestion and now incorporate this finding in section “Linking variation in chromatin features with transcriptional output using SVMs”: “Furthermore, the SVM does not require extreme values or outliers, and hence the overall approach could be of rather general applicability. As a performance verification, we applied the SVM to a dataset containing only the cell lines that could generate PGCLCs with high or intermediate efficiency, and while the performance is slightly reduced, it remains satisfactory (accuracy 75%; Supplementary Fig. 6H).”

If the SVM on the 6 lines does not predict a binary switch in H3K27me3 to be predictive could the authors incorporate DNA methylation and H3K4me1 from the same publication as the chromatin accessibility. Such an analysis may also assist future users of the SVM method to assess the number of parameters required to separate closely related phenotypes.

See previous answer. We note that DNA methylation data for our hiPSC panel is not available; it is not part of the study that the reviewer mentions (https://link.springer.com/article/10.1186/s13059-025-03658-8). Although H3K4me1 data is available in Todd et al., we did not find that this data improved the ability of our model to make successful predictions (see reply to Reviewer #2, point 1).

Most gene regulation occurs at the level of the enhancer, restricting analysis to promoter associated histone modifications is limiting.

We thank the Reviewer for raising this very valid point. Please see response to Reviewer #2, point 1.

One puzzling piece of data is the very high 60% of PGCLCs on day 1 of differentiation (Fig 1E) in the competent cell lines. BLIMP1 is expressed in hiPSCs, calling into question whether the initial differentiation into pre-ME was successful.

We think there is a misunderstanding regarding the experimental timeline. Day 1 of differentiation in Fig. 1E refers to one day after PGCLC induction from the pre-ME stage following the addition of BMP4, SCF, LIF, and EGF (see schematic in Fig. 1A). We have revised the text to make this clearer. Furthermore, BLIMP1 (PRDM1) is not expressed in hiPSCs. To demonstrate this, we now show the expression levels of BLIMP1 (PRDM1), B2M (low to mid-level expression in most human cell types), SOX2 (highly expressed pluripotency marker), and HOXC10 (differentiation marker that is not expressed in PSCs) across our cell line panel. At this scale, BLIMP1/PRDM1 expression is not detectable. When SOX2 is omitted from this bar plot, the very low expression levels of BLIMP1/PRDM1 become apparent, as it is close to the levels for the differentiation marker HOXC10. We conclude that BLIMP1/PRDM1 is expressed at extremely low levels across our ten hiPSC lines.

The H3K27me3 and H3K9me3 signals are integrated over the entire gene as inputs into the SVM, however PCA analysis to separate the cell lines is only shown for the promoter

This is not quite correct. For the PCA analysis for the histone marks and ATAC-seq, we used both the promoter region (Fig. 2C, Supplementary Fig. 5B) and the gene body (Supplementary Fig. 5A), with similar results. For the SVM, for H3K27me3 and H3K9me3, we primarily used the entire gene region, but we also tested other regions (Supplementary Fig. 6A), with similar or slightly inferior results.

SVMs have been used to predict enhancers from epigenomic data PMID: 22328731 and to classify cancers PMID: 11120680. Applying SVM as classifier for gene expression prediction is not very novel.

We thank the Reviewer for raising this point. We did not claim that the use of SVMs was itself novel. It has certainly been used in other contexts, as the reviewer points out, to predict enhancers, for cancer classification, and to predict expression patterns. In fact, SVMs had already been used to predict gene expression from chromatin features (Cheng et al, 2011; already cited in our manuscript). What is novel in our work is the reverse-engineering of the method to extract mechanistic information about each gene (i.e., assign a chromatin feature set relevant to the changes in expression). This computational methodology, in conjunction with the rich experimental dataset produced, allows us to classify differentially expressed genes in terms of the chromatin features that enable prediction of transcription. This highlights the differences between cell lines and enables further downstream analysis such as, mechanistic models of histone modification dynamics and the prediction of iPSC differentiation efficiency. We have rewritten the Introduction to the manuscript to better emphasise these points.

The biological insights are limited. For example, the observation that " a variety of forms of transcriptional regulation" Fig 4B. It is well known that H3K27me3 decorates lineage specifying genes and is part of the bivalent domain with H3K4me3. The anti-ATAC category could represent locations where a repressor is bound DNA which would also result in increased accessibility and is not a surprising result.

We believe our work does offer significant biological insights. While we agree that it is well known that H3K27me3 decorates lineage specifying genes, it was not previously known that digital Polycomb dysregulation at specific loci was a key feature controlling the ability of pluripotent cell lines to differentiate properly. In addition, we have been able to identify a core set of genes whose H3K27me3 profiles are highly informative for differentiation efficiency. Moreover, we are able to explain the variation in H3K27me3 levels by simple, quantitative, mathematical model.

Finally, the anti-ATAC category is a minor finding and not one of the central conclusions of this paper. Nevertheless, we appreciate the Reviewer’s suggestion and have incorporated this possible interpretation into section “Chromatin accessibility does not always correlate with transcription”.

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Referee #3

Evidence, reproducibility and clarity

Human induced pluripotent stem cells (iPSCs) have variable differentiation capability and can demonstrate bias toward specific lineages. In this manuscript, try to identify epigenetic features that may explain biased differentiation. They perform RNAseq and CUT and TAG for H3K4me3, H3K27me3 and H3K9me3 on 10 hiPSC lines which some of which show a opposing differentiation potential toward primordial germ cell like cells (PGCLCs) or definitive endoderm (DE). Using a support vector machine per gene that is variably expressed, they identify combinations of epigenetic marks and accessibility that could explain the change in expression. They identify H3K27me3 as a binary switch with high enrichment of this modification, predicting repression.

The major claim that among histone modifications that have been profiled in this manuscript, H3K27me3 is the most predictive for expression is supported by the analysis. However the analysis may be skewed because the RNAseq and the H3K27me3 difference are driven by the extreme skewing of the 3 cell lines Yoch6, Sojd3 and Kucg (Fig 2A, 2C and 6A). Two of these lines cannot form EBs at all, a major failure in their pluripotent characteristics. Further, one of the lines that can form EBs, fails to make PGCLCs but can differentiate into DE, Letw5 has neither the RNA profile nor the H3K27me3 profile of the skewed iPSC lines. Therefore, whether H3K27me3 truly influences phenotype at least in terms of PGCLC and DE differentiation of iPSCs is not supported by the analysis in the manuscript. Further analysis that may support their claim

1. What are the predictions from applying SVM to data from only the 6 cell lines Podx, Kolf2, Kolf3, Bima 1, Qolg1, Wibj2. The DE differentiation potential will also have to be measured for each of these cell lines.
2. The above analysis may also shed light on how extreme the input parameters must be for SVM to be a good classifier? Such an analysis may also assist future users of the method to assess whether SVM would be useful for their datasets.
3. If the SVM on the 6 lines does not predict a binary switch in H3K27me3 to be predictive could the authors incorporate DNA methylation and H3K4me1 from the same publication as the chromatin accessibility. Such an analysis may also assist future users of the SVM method to assess the number of parameters required to separate closely related phenotypes.
4. Most gene regulation occurs at the level of the enhancer, restricting analysis to promoter associated histone modifications is limiting.
5. One puzzling piece of data is the very high 60% of PGCLCs on day 1 of differentiation (Fig 1E) in the competent cell lines. BLIMP1 is expressed in hiPSCs, calling into question whether the initial differentiation into pre-ME was successful.
6. The H3K27me3 and H3K9me3 signals are integrated over the entire gene as inputs into the SVM, however PCA analysis to separate the cell lines is only shown for the promoter The recommended analysis above is not substantial because it only requires missing DE differentiation in terms of experiments. Data and methods have sufficient detail to be reproduced.

Referee cross-commenting

I agree with the other reviewer comments

Significance

The data generated and differentiation are useful for the hiPSCs community.

SVMs have been used to predict enhancers from epigenomic data PMID: 22328731 and to classify cancers PMID: 11120680. Applying SVM as classifier for gene expression prediction is not very novel.

The biological insights are limited. For example, the observation that " a variety of forms of transcriptional regulation" Fig 4B. It is well known that H3K27me3 decorates lineage specifying genes and is part of the bivalent domain with H3K4me3. The anti-ATAC category could represent locations where a repressor is bound DNA which would also result in increased accessibility and is not a surprising result.

Specialized for an audience of epigenetics and iPSC.

My expertise is in epigenetics, cell identity specification and pluripotency. I do not have expertise to evaluate accuracy of compuational method.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, Miangolarra and colleagues explore functional heterogeneity in human iPSC, a characteristic which can impact on their translational utility. They characterise the capacity of ten iPSCs lines to differentiate in to either mesendoderm, primordial germ cell-like cells and/or definitive endoderm and explore the mechanistic basis of this potential by interrogating their transcriptional and epigenetic status using 'omics' approaches and mathematical modelling. The authors find that the iPSCs group based on differentiation capacity and their underlying transcriptional and epigenetic status, partitioning that is most tightly associated with H3K27me3 patterns that are binary in nature.

Key findings/outputs of the study: Inter-iPSC line variability in the differentiation tendency is reciprocal between PGCLCs and definitive endoderm.

Distinct differentiation/or maintenance capacities of iPSCs are governed/marked by altered H3K27me3 signatures.

Grouping of iPSC lines based on developmental capacity is demarcated by transcription profiles and their corresponding H3K27me3 patterns.

Chromatin accessibility at gene promoters is a relatively poor predictor of transcriptional status.

The transcription state of iPSCs is a good predictor of gene expression patterns following subsequent differentiation.

H3K27me3 shows a somewhat binary relationship with gene expression which the authors liken to a digital signature that is consistent with the read-write activity of PRC2.

A subset of H3K27me3 targets is strongly predictive of transcriptional state and differentiation capacity.

A machine learning approach that utilises integrated epigenome status to predicts high or low gene expression with high accuracy in iPSCs.

The authors present a large amount of high-quality data that is of broad interest to various fields as it provides: 1. Mechanistic insight into the epigenetic basis of gene regulation in human pluripotent cells. 2. A metric for assessing differentiation potential of pluripotent cells with important translational implications 3. Machine learning tools that could be of broad utility to the field of epigenetics and gene regulation (provided as well annotated code in Github).

Whilst this reviewer is unable to provide an in-depth assessment of the machine learning approach presented, the modelling and data handling is accessible. Whilst this study does not provide substantial biological insights, the collective works is of broad utility and interest to various fields and I believe can be published once the following minor comments/concerns are addressed.

Comments

Figure 2B. Are all GO terms shown in the figure or are these just the top terms? If this is a suset then all terms should be provided as a supplemental table. If this is all significant terms, this is relitavely modest considering the number of DEGs (712) and is probably due to the fact that DEGs are derived from all comparisons and so could be diluted by the presence of multiple opposing effects. If this is the case, you could identify DEGs that define the PCA groupings and then re-run the GO analysisto potentially provide a better definition of the functional differences between groups of cell lines. Chromatin accessibility at gene promoters is a poor predictor of transcription, but it is likely that accessibility at distal regions (e.g putative enhancers) might be a better predictor. Did the authors look at this? This possibility should at least be mentioned when discussing the ATC-seq data and the lack of correlation with transcription.

Fig 1C. Statistic overview at end of legend should be moved under section describing panel C in the legend.

'Furthermore, the transition value of 30% enables repression to be stably maintained even after DNA replication, when, on average, histone modification levels will be transiently halved'. Whilst this is potentially true and a plausible interpretation, you cannot exclude that the signal is not derived from different cell populations in the culture due to cellular heterogeneity such as cell cycle or spontaneous differentiation. This possibility should be noted in the text.

'Higher values indicate stronger correlation or anticorrelation and, thus, stronger differences between cell lines.' I don't believe this makes sense as written. Do the authors mean stronger partitioning of different iPSC lines into clusters?

'We found that 60% of the DEGs in pre-ME were also DEGs in hiPSCs'. This needs to be made clearer. Do the authors mean DEGs between iPSCs following differentiation or DEGs between undifferentiated iPSCs and their differentiated derivatives? The former suggests that the iPSCs are already partially differentiated and that differentiation in promoted or constrained by this starting state whilst the latter would suggest that some lines are skewed towards the mesendoderm.

'Finally, histone marks in the iPSC state were also predictive of expression in the pre-ME state, albeit with slightly lower accuracy than for the iPSC state (Supplementary Fig. 8C, D), which may indicate the existence of an epigenetic memory system that is maintained during differentiation.' Or the retention of an epigenetic signature that failed to be erased during the initial generation of the iPSCs.

'To minimise the risk of overfitting, only reliable targets were retained'. Whilst this is outlined in the methods as stated, a summary of what this means should be included in the body text.

Referee cross-commenting

I agree with the other reviewer's comments.

Significance

The presented manuscript investigates the molecular basis of developmental potential heterogeneity in human iPSCs. The study is clear, well presented and provides sufficient detail on methodology, reagents and computational tools to allow reproducibility. The claims made are supported by the data and analysis.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

Summary

This study investigates the molecular basis of inter-line variability in human iPSC differentiation efficiency. The authors profile ten HipSci consortium hiPSC lines for transcriptome (RNA-seq), chromatin accessibility (ATAC-seq, from a prior study), and three histone modifications (H3K4me3, H3K9me3, H3K27me3) by CUT&Tag. They develop an SVM-based computational pipeline to link epigenomic variation to transcriptional differences across lines. The central findings are that H3K27me3 variation shows the most consistent inter-line differences, displays a bimodal (digital ON/OFF) distribution consistent with a mathematical model of PRC2 read-write feedback, and that a small set of Polycomb-regulated genes can predict differentiation efficiency into both PGCLCs and dopaminergic neurons. The authors also show that these transcriptional differences propagate into the pre-mesendoderm intermediate state, suggesting epigenetic memory during early lineage commitment. This is overall a very good study, with interesting and novel findings for the field. However, some issues should be addressed before publication:

Major Comments

1. The inverse relationship between PGCLC and DE efficiency is intriguing but under-explored. The observation that lines efficient for PGCLCs (Podx1, Kolf2) are poor at DE differentiation, and vice versa, is one of the key findings. Yet this is presented almost in passing. It would strengthen the paper considerably if the authors discussed whether their Polycomb-regulated gene set predicts DE efficiency with an inverse sign, and whether the logistic regression model can be tested on the DE data directly.
2. The mathematical model is elegant but the choice to vary parameter E across cell lines needs stronger justification. The model assumes that inter-line differences are driven by variation in the overall rate of H3K27 methylation (parameter E). This is a reasonable starting assumption, but the authors should discuss alternative scenarios more explicitly. Could variation in demethylase activity, PRC2 recruitment strength, or replication timing equally well explain the data? The fact that EPOP is differentially expressed is mentioned as a potential mechanistic candidate for modulating E, which is compelling, but the link remains correlative. The authors should be more cautious in their language here, stating that EPOP "may be sufficient to completely switch the transcriptional regulation" goes beyond what the data show.
3. The predictive model for differentiation efficiency is promising but the PGCLC training set is too small for confident generalisation claims. The authors acknowledge this (109 features, ~21 data points), and the L2 regularisation is appropriate. However, the claim of 91% accuracy with a 95% CI of 78-100% on the PGCLC data should be presented more cautiously. With such a small dataset, the confidence interval is very wide. The more convincing validation comes from the DN data (143 lines from Jerber et al.), where the model trained on PGCLC data performs comparably to the full-transcriptome model. This cross-fate generalisation is quite strong and should be emphasised more prominently as the primary evidence for the validity of the model.
4. The claim of "epigenetic memory" during differentiation (iPSC to pre-ME) is suggestive but would benefit from additional analysis. The authors show that 60% of pre-ME DEGs overlap with iPSC DEGs, and that H3K27me3-cluster genes maintain their expression patterns. However, 60% overlap could partly reflect genes that are simply not regulated during the short 12-hour pre-ME induction. To strengthen this claim, the authors should compare the overlap rate for H3K27me3-cluster genes specifically versus other clusters. If Polycomb targets show significantly higher overlap than, for example, K4&ATAC genes, this would more convincingly support a Polycomb-specific memory mechanism.
5. Lack of genetic background analysis. Ten lines from nine donors will harbour substantial genetic variation. The authors note that genetic variation has been linked to iPSC heterogeneity but do not analyse whether the three "outlier" lines (Kucg2, Sojd3, Yoch6) share genetic features. For instance, common variants at PRC2 component loci, EPOP regulatory variants, or structural variants that might alter H3K27me3 domain boundaries. The HipSci consortium provides genotyping data for these lines. A targeted analysis of variants at Polycomb-related loci would be feasible and could either strengthen the epigenetic interpretation or reveal a genetic confounder.

Minor Comments

• The promoter definition ({plus minus}1 kb from gene start) is non-standard; most studies use a window upstream of the TSS rather than gene start. The authors mention they confirmed robustness to an alternative definition (-1 kb to gene start) but do not show this data. It should be included in the supplement.
• For CUT&Tag, no spike-in normalisation is mentioned. Given that the key conclusions is based on quantitative comparisons of H3K27me3 levels across cell lines, the absence of spike-in controls is a potential concern. The authors should discuss whether technical variation between CUT&Tag libraries could contribute to the observed bimodality. At minimum, the correlation between replicates for H3K27me3 should be shown (presumably it is high, but this should be documented).
• The statistical test for the PGCLC/H3K27me3 overlap (p < 0.04, combinatorial argument) assumes independence of lines, which may not hold if genetic relatedness or batch effects are present. This should be noted.
• Figure 6A: the heatmaps for H3K4, ATAC and H3K27 are shown side by side but at apparently different scales; this should be clarified or made consistent.

Referee cross-commenting

I agree with the comments from other reviewers

Significance

This paper makes a primarily conceptual advance in understanding why iPSC lines differ in their differentiation capacity. The key insight is that Polycomb regulation operates in a digital (bistable) fashion at specific loci, and that this digital behaviour both explains the sharpness of inter-line transcriptional differences and enables prediction of differentiation outcomes from a small gene set. This work will be of broad interest to the stem cell biology community, particularly those working on iPSC-based disease modelling and cell therapy where line-to-line variability is a major practical challenge. The mathematical modelling component will appeal to quantitative/systems biologists interested in chromatin regulation. The computational pipeline may find applications beyond iPSCs, in any setting where epigenomic and transcriptomic data are available across multiple conditions.

Reviewer expertise: Developmental biology, chromatin regulation, iPSC differentiation, epigenetics.

Regime de caixa para receitas; Regime de competência para despesas.

Conversão do crédito de moeda estrangeira da dívida ativa

Deve constar obrigatoriamente no termo de Inscrição em dívida ativa

same in my previous comment about "rusty"

说明书要记录现有技术

电话聊过，知道民教网，之前22k，期望25k，想要换一个更高的平台

Under the Bureau of Internal Revenue (BIR) guidelines and the TRAIN Law, any individual earning a monthly taxable income of ₱20,833 or below is entirely tax-exempt.

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Resource: (MMRRC Cat# 034832-JAX,RRID:MMRRC_034832-JAX)

Curator: @AleksanderDrozdz

SciCrunch record: RRID:MMRRC_034832-JAX

What is this?

DOI: 10.1177/13872877261444307

Resource: GraphPad Prism (RRID:SCR_002798)

Curator: @scibot

SciCrunch record: RRID:SCR_002798

What is this?

DOI: 10.1177/13872877251343321

Resource: (MMRRC Cat# 034832-JAX,RRID:MMRRC_034832-JAX)

Curator: @AleksanderDrozdz

SciCrunch record: RRID:MMRRC_034832-JAX

What is this?

DOI: 10.1155/humu/5598948

Resource: (IZSLER Cat# BS CL 87, RRID:CVCL_0574)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0574

What is this?

DOI: 10.1111/fwb.70135

Resource: Brigham Young University College of Life Sciences DNA Sequencing Center (RRID:SCR_017781)

Curator: @bandrow

SciCrunch record: RRID:SCR_017781

What is this?

DOI: 10.1101/2024.12.31.630967

Resource: RRID:MMRRC_000213-UCD

Curator: @AleksanderDrozdz

SciCrunch record: RRID:MMRRC_000213-UCD

What is this?

DOI: 10.1073/pnas.2528664123

Resource: RRID:MMRRC_009981-UCD

Curator: @AleksanderDrozdz

SciCrunch record: RRID:MMRRC_009981-UCD

What is this?

DOI: 10.1038/s44318-024-00290-3

Resource: RRID:BDSC_26717

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_26717

What is this?

DOI: 10.1038/s44318-024-00290-3

Resource: RRID:BDSC_44249

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_44249

What is this?

DOI: 10.1038/s44318-024-00290-3

Resource: RRID:BDSC_5905

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_5905

What is this?

DOI: 10.1038/s44318-024-00290-3

Resource: RRID:BDSC_10154

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_10154

What is this?

DOI: 10.1038/s44318-024-00290-3

Resource: RRID:BDSC_6868

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_6868

What is this?

DOI: 10.1038/s44318-024-00290-3

Resource: RRID:BDSC_6469

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_6469

What is this?

DOI: 10.1038/s44318-024-00290-3

Resource: RRID:BDSC_86108

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_86108

What is this?

DOI: 10.1038/s44318-024-00290-3

Resource: RRID:BDSC_4517

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_4517

What is this?

DOI: 10.1038/s44318-024-00290-3

Resource: RRID:BDSC_6470

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_6470

What is this?

DOI: 10.1038/s44318-024-00290-3

Resource: RRID:BDSC_4516

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_4516

What is this?

DOI: 10.1038/s44318-024-00290-3

Resource: RRID:BDSC_99568

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_99568

What is this?

DOI: 10.1038/s44318-024-00290-3

Resource: RRID:BDSC_51190

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_51190

What is this?

DOI: 10.1038/s44318-024-00290-3

Resource: RRID:BDSC_64290

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_64290

What is this?

DOI: 10.1038/s44318-024-00290-3

Resource: RRID:BDSC_11981

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_11981

What is this?

DOI: 10.3389/fendo.2026.1830329

Resource: (MMRRC Cat# 042277-JAX,RRID:MMRRC_042277-JAX)

Curator: @AleksanderDrozdz

SciCrunch record: RRID:MMRRC_042277-JAX

What is this?

DOI: 10.3389/fendo.2026.1830329

Resource: Human Islet Research Network (HIRN) (RRID:SCR_014393)

Curator: @scibot

SciCrunch record: RRID:SCR_014393

What is this?

DOI: 10.3389/fendo.2026.1830329

Resource: HIRN Human Pancreas Analysis Program (RRID:SCR_016202)

Curator: @scibot

SciCrunch record: RRID:SCR_016202

What is this?

DOI: 10.1038/s41467-025-56766-1

Resource: RRID:WB-STRAIN:WBStrain00034668

Curator: @olekpark

SciCrunch record: RRID:WB-STRAIN:WBStrain00034668

What is this?

DOI: 10.1038/s41467-025-56766-1

Resource: RRID:WB-STRAIN:WBStrain00032492

Curator: @olekpark

SciCrunch record: RRID:WB-STRAIN:WBStrain00032492

What is this?

DOI: 10.1038/s41467-025-56766-1

Resource: RRID:WB-STRAIN:WBStrain00034669

Curator: @olekpark

SciCrunch record: RRID:WB-STRAIN:WBStrain00034669

What is this?

DOI: 10.1038/s41467-025-56766-1

Resource: RRID:WB-STRAIN:WBStrain00031590

Curator: @olekpark

SciCrunch record: RRID:WB-STRAIN:WBStrain00031590

What is this?

DOI: 10.1038/s41467-025-56766-1

Resource: RRID:WB-STRAIN:WBStrain00036386

Curator: @olekpark

SciCrunch record: RRID:WB-STRAIN:WBStrain00036386

What is this?

DOI: 10.1038/s41467-025-56766-1

Resource: (WB Cat# WBStrain00004840,RRID:WB-STRAIN:WBStrain00004840)

Curator: @olekpark

SciCrunch record: RRID:WB-STRAIN:WBStrain00004840

What is this?

DOI: 10.1038/s41467-025-56766-1

Resource: RRID:WB-STRAIN:WBStrain00034069

Curator: @olekpark

SciCrunch record: RRID:WB-STRAIN:WBStrain00034069

What is this?

DOI: 10.1038/s41467-025-56766-1

Resource: RRID:WB-STRAIN:WBStrain00030219

Curator: @olekpark

SciCrunch record: RRID:WB-STRAIN:WBStrain00030219

What is this?

DOI: 10.1038/s41467-025-56766-1

Resource: RRID:WB-STRAIN:WBStrain00035557

Curator: @olekpark

SciCrunch record: RRID:WB-STRAIN:WBStrain00035557

What is this?

DOI: 10.1038/s41467-025-56766-1

Resource: RRID:WB-STRAIN:WBStrain00031577

Curator: @olekpark

SciCrunch record: RRID:WB-STRAIN:WBStrain00031577

What is this?

DOI: 10.1038/s41467-025-56766-1

Resource: RRID:WB-STRAIN:WBStrain00041079

Curator: @olekpark

SciCrunch record: RRID:WB-STRAIN:WBStrain00041079

What is this?

DOI: 10.1038/s41467-025-56766-1

Resource: (WB Cat# WBStrain00041969,RRID:WB-STRAIN:WBStrain00041969)

Curator: @olekpark

SciCrunch record: RRID:WB-STRAIN:WBStrain00041969

What is this?

DOI: 10.1016/j.xpro.2024.103438

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @bdscstockkeepers

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1016/j.pneurobio.2026.102931

Resource: (MMRRC Cat# 033018-UCD,RRID:MMRRC_033018-UCD)

Curator: @AleksanderDrozdz

SciCrunch record: RRID:MMRRC_033018-UCD

What is this?

DOI: 10.1016/j.isci.2026.116503

Resource: Mutant Mouse Regional Resource Center (RRID:SCR_002953)

Curator: @AleksanderDrozdz

SciCrunch record: RRID:SCR_002953

What is this?

DOI: 10.1016/j.celrep.2026.117603

Resource: (IMSR Cat# JAX_024857,RRID:IMSR_JAX:024857)

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:024857

What is this?

DOI: 10.1016/j.celrep.2026.117603

Resource: RRID:IMSR_CRL:027

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_CRL:027

What is this?

DOI: 10.1016/j.cell.2025.04.007

Resource: RRID:MMRRC_043532-UCD

Curator: @AleksanderDrozdz

SciCrunch record: RRID:MMRRC_043532-UCD

What is this?

DOI: 10.1038/s41467-026-70364-9

Resource: Mutant Mouse Regional Resource Center (RRID:SCR_002953)

Curator: @AleksanderDrozdz

SciCrunch record: RRID:SCR_002953

What is this?

DOI: 10.1016/j.canlet.2026.218694

Resource: (RRID:CVCL_J962)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_J962

What is this?

DOI: 10.1016/j.bbi.2025.07.017

Resource: RRID:MMRRC_048130-UCD

Curator: @AleksanderDrozdz

SciCrunch record: RRID:MMRRC_048130-UCD

What is this?

DOI: 10.1002/cam4.72059

Resource: (NCI-DTP Cat# MCF7, RRID:CVCL_0031)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0031

What is this?

DOI: 10.1002/advs.76054

Resource: GraphPad Prism (RRID:SCR_002798)

Curator: @areedewitt04

SciCrunch record: RRID:SCR_002798

What is this?