Joint epigenome profiling reveals cell-type-specific gene regulatory programmes in human cortical organoids

Gene expression is regulated by multiple epigenetic mechanisms, which are coordinated in development and disease. However, current multiomics methods are frequently limited to one or two modalities at a time, making it challenging to obtain a comprehensive gene regulatory signature. Here, we describe a method—3D genome, RNA, accessibility and methylation sequencing (3DRAM-seq)—that simultaneously interrogates spatial genome organization, chromatin accessibility and DNA methylation genome-wide and at high resolution. We combine 3DRAM-seq with immunoFACS and RNA sequencing in cortical organoids to map the cell-type-specific regulatory landscape of human neural development across multiple epigenetic layers. Finally, we apply a massively parallel reporter assay to profile cell-type-specific enhancer activity in organoids and to functionally assess the role of key transcription factors for human enhancer activation and function. More broadly, 3DRAM-seq can be used to profile the multimodal epigenetic landscape in rare cell types and different tissues.

Fig. 1B and C should show a negative control where the sample has not been treated with the GpC methyltransferase.
For extended data fig.1B, are the authors using all GpC's in the genome or just those over open chromatin sites?If they are using all GpCs, having 50% GpC methylation seems like it has a lot of non-specific methylation occurring.They should make this plot showing the saturation of methylation over known open chromatin regions and outside of known open chromatin regions to give the reader an idea of the specificity of this approach.This is an important issue as high levels of non-specific GpC labelling may alter the "normal" CpG methylation profiles and make the method less useful.
Reviewer #2 (Remarks to the Author): Multiple epigenetic mechanisms such as histone marks, DNA methylation, and chromatin accessibility may simultaneously regulate gene expression and TF binding.Current multi-omic methods are yet to simultaneously profile all three epigenetic layers (3D genome, chromatin accessibility and DNA methylation).Authors developed a method called 3DRAM-seq that will simultaneously measure (3D genome, RNA, Accessibility and Methylation sequencing) at a single cell resolution and describe the protocol by which performing these modalities are possible.Authors used three biological replicates in mouse ESCs to perform and developed the 3DRAM-seq method.The replicates unanimously revealed lower DNA methylation levels and higher accessibility for transcription start sites compared to repressed genes which is inline with previous studies.The protocol also showed high reproducibility for gene expression.They looked at Sox2 locus for confirming the protocol can capture all three epigenetic modalities.The authors benchmarked the protocols to other multiomic approaches including Methyl-3C, Methyl-HiC.Reportedly, 3DRAM-seq measurement of DNA-methylation was highly correlated with the other methods.Moreover, 3DRAM-seq had the highest proportion of uniquely mapped reads and total Hi-C contacts.
Next, the authors coupled 3DRAM-seq with immunoFACS and purified glia cells (RGC) and intermediate progenitor cells (IPC) from human cortical organoids.Authors reported upregulation of genes involved in neuronal differentiation as opposed to downregulation of RGC specific genes which is expected.Radial glial cells specialize in the developing nervous system in vertebrates and intermediate progenitor cells are transient amplifying cells in the developing cerebral cortex.
They report DAR (differentially accessible regions) in each organoid, and that there is an uncoupling between DNA methylation and 3D DNA looping for accessibility that may be dependent on TF binding regulation.Authors translate this phenomena as the fact that high methylation levels does not always result in low accessibility and vice versa.
Next, authors investigated the roles of two different classes of transposable elements (TE); MER130 and UCON31 that are associated with epigenetic differences across modalities.Despite an increase in accessibility, only MER130 showed a decrease in DNA methylation which further supports their claim of uncoupling between the two modalities.
Authors developed a novel variant of MPRA by coupling electroporation in organoids with FACS to investigate cell-type-specific regulation.Then they performed 3DRAM-seq in each cell type to investigate the role of specific TF in regulating enhancers in human neurogenesis.The authors found that enhancer activity is correlated with accessibility, but not all accessible regions drive gene expression.
Major comments: 1. Authors develop the protocol on mouse embryonic stem cells -Although they perform the protocol in human cortical organoids later to detect cell type specific TF binding, can authors mention if the protocol takes into account the regulatory differences between mouse stem cells vs. human brain organoids 2. Authors provide sox2 locus for confirming the protocol can capture all three epigenetic modalities as well as gene expression levels, emphasizing sox2 exhibits short distances at enhancer-promoter loops: "the resolution provided by 3DRAM-seq allowed to observe all three epigenetic modalities as well as gene expression levels even at very short distances at enhancer-promoter loops, such as the Sox2 locus" -It would be helpful for the authors elaborate on how enhancer-promoter proximity and distance matters in capturing epigenetic status as well as gene expression levels -What is the minimum and maximum distance between enhancer and promoter over which 3DRAMseq can successfully capture cell-type regulatory landscape?3. Combining 3DRAM-seq with immunoFACS and RNA-Seq in RGC and IPC to look at epigenome landscape -Authors reasoned that to profile specific cell types in complex systems such as brain development, 3DRAM-seq can be coupled with immunoFAC-sorting of RGCs and IPCs from human cortical organoids -Would be interesting to see if the protocol is also able to identify regulatory TFs associated with mature neuronal cell types (e.g.Purkinje cells) in addition to stem cells involved in a developing brain.
4. Authors compare the performance of 3DRAM-seq coupled with ImmunoFACS purified RGC and IPC from human cortical organoids to their earlier results in mouse ESC, where replicates demonstrated a high bisulfite conversion rate, uniformly high coverage, and a high pairwise correlation across modalities; "Reminiscent of the previous results in mESC, all replicates showed a high context independent bisulfite conversion rate, …." -Can authors reason that this result is mainly due to the biological reasons and their protocol is not over-reporting the conversion efficiency and coverage?Can they provide an example of a gene where the coverage was low and that was consistent with previously established protocols such as Methyl-HiC?-Also, often when comparing 3DRAM-seq results in human cortical organoids, authors refer back to findings in mouse ESCs.Can authors comment on how comparable the gene regulatory network is between human cortical organoids and mouse ESCs? 5. Why did the authors choose UCON31 and MER130 as two specific classes of TE to study chromatin accessibility?
6. Authors performed MPRA to show the utility of their protocol works not only in bulk nuclei but can also be extended to cell-type specific, can they perform a single-cell experiment (rna-seq, methylation, etc) to profile cell-type-specific enhancer activity?7. Some figures are missing scale bar, eg.x axis y axis annotation (1.6 in the top right corner in fig 1E is hard to see except mention in legend ) 8. How was the protocol optimized for fixed cells?It is often difficult to separate clusters on FACS sort.The paper acknowledged their success as usually difficult to achieve but changes were not apparent.Protocol says cells can go directly into 3DRAM-seq before FACS sorting.It seems it would be beneficial to sort before to enrich for specific cell types for such a high resolution multi-omic approach.Is this an option or was it tried during adaptation of the protocol?
Reviewer #3 (Remarks to the Author): This manuscript applied robust epigenomic/3D-genome profiling approaches and functional validation methods previously described in Noack et al., 2022 (from the same group) Nature Neuroscience to human brain organoids.Conceptually and also methodologically, this manuscript is quite similar to the published Noack et al., 2022 paper.The addition of M.CviPI-based chromatin accessibility profiling to Methyl-HiC is a relatively modest improvement that has been demonstrated multiple times by various groups.Although cerebral organoids have been shown as a sound model for brain development, there have also been compelling arguments that organoids fall short in modeling the specification of cell subtypes (for example, Bhaduri et al., 2020, PMID: 31996853).So I would view the current manuscript as a bit less significant than Noack et al., 2022, especially from a data resource perspective), since Noack et al, 2022 profiled primary mouse brain tissues, whereas the present study only included data generated from organoids.Therefore although I think the presented works are solid, the manuscript might be a better fit for a more specialized journal.
More specific comments 1.Technically the RNA profile was not generated from the same nuclei sample as those used by the Methyl-HiC assay.It might be helpful to clarify that in Fig. 1A schematics.
2.Could the authors further clarify the low overlapping between DMRs and DARs (1% for decreased DNA methylation, or 0.1% for increased DNA methylation).The degree of overlap seems to be much lower than what the meta-analysis in Fig. 4B suggests.
3. The observation that LHX2-associated binding does not associate with DNA demethylation could be explained by that these regions are already lowly-methylation (<25%) to start with, whereas regions bound by Neurog2 show a methylation level of 50-60%.Therefore it is unclear whether the result can be generalized to an uncoupling of methylation and other data modalities.The genomic contexts (promoters vs. enhancers, GpC contents, etc) of Lhx2 and Neurog2 binding sites should be further dissected.
4. How many copies of MER130 and UCON31 TEs were found in the human genome?This will help to distinguish whether the p-value difference shown in Fig. 5C-D (significant for MER130, not significant for UCON31) was driven by a difference in the effect size or sample size.
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Author Rebuttal to Initial comments Decision Letter, first revision:
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Dear Dr Bonev, Your manuscript, "Joint epigenome profiling reveals cell-type-specific gene regulatory programs in human cortical organoids", has now been seen by all of our original referees, who are experts in 3D-genome (referee 1); Chromatin accessibility (referee 2); and epigenetics (referee 3).As you will see from their comments (attached below) they find this work of interest, but have raised some important points.Although we are also very interested in this study, we believe that their concerns should be addressed before we can consider publication in Nature Cell Biology.
Nature Cell Biology editors discuss the referee reports in detail within the editorial team, including the chief editor, to identify key referee points that should be addressed with priority, and requests that are overruled as being beyond the scope of the current study.To guide the scope of the revisions, I have listed these points below.We are committed to providing a fair and constructive peer-review process, so please feel free to contact me if you would like to discuss any of the referee comments further.
In particular, it would be essential to: A) Include all necessary controls for all experiments as outlined by reviewer#1 B) Clarify data and analysis throughout the manuscript, as recommended by reviewer#1 C) Tone down claims around developing a novel variant of a massively parallel reporter assay (MPRA) D) All other referee concerns pertaining to strengthening existing data, providing controls, methodological details, clarifications and textual changes, should also be addressed.E) Finally please pay close attention to our guidelines on statistical and methodological reporting (listed below) as failure to do so may delay the reconsideration of the revised manuscript.In particular please provide: -a Supplementary Figure including unprocessed images of all gels/blots in the form of a multi-page pdf file.Please ensure that blots/gels are labeled and the sections presented in the figures are clearly indicated.
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In contrast, although we agree with referee 3 that physiological relevance (primary cells) would provide valuable insights, we consider this point to be beyond the scope of the present study.Thus, addressing it experimentally will not be necessary for reconsideration of the manuscript at this journal.
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We hope that will find our referees ' comments, and  Reviewer #1: Remarks to the Author: Noack, Vangelisti et al. present a revised version of their manuscript describing 3DRAM-seq and its application in brain organoids.I find the revised manuscript to be a substantial improvement over the original submission.The additional analysis comparing different RNA-seq and open chromatin modalities goes a long way to convincing me of the quality of the data.Further, the new analysis of co-accessibly motifs greatly improves the insights from the manuscript and indeed helps illustrate the utility of the 3DRAM-seq assay.The results of this analysis are pretty interesting, though somewhat unexpected (I would have assumed you would see greater than expected coaccessibility, I'm still trying to wrap my head around what this means).I still believe that there are some substantial issues that remain to be address, most of which surrounds the new analysis of coaccessibility, but I believe that with adequate revisions this study would ultimately be appropriate for publication in Nature Cell Biology.I have divided my comments into major and minor points below: Major Comments: I have several points related to the co-accessibility and single molecule interaction analysis.First, the methods for this section ("Co-accessibility and co-methylation analysis at single-molecule resolution") do not provide sufficient depth to either understand or recapitulate the analyses performed in the manuscript.They mostly describe of some of the bioinformatic approaches used to identify reads, but they don't go into how any of the clustering was performed or any statistical tests presented.
Related to above, for the "co-accessibility pattern" testing (line 170-171), it isn't clear at all how this analysis was performed.For example, if their randomized control is 5% and their observed is 19%, how do they perform the testing and get an odds ratio of 0.94?They need more details here on what was done.
I think the authors should comment in the discussion on the fact they don't see greater than expected co-accessibility of CTCF or CRE-TSS during interactions.It is a somewhat unexpected result (at least in my mind) and I think providing some level of interpretation or speculation about why this is observed would be helpful.I think that this is an important result more broadly for the single-cell community, as methods for single-cell ATAC-seq for linking CRE to genes rely on such "co-accessibility" measurements (PMID 30078726), but this makes it clear that co-accessibility (at least within cell types) doesn't really match with other molecular features that relate to enhancer-promoter connectivity.
Can the authors further explain the analysis in Figure 5J and K? This is a two part question, 1) Are they first just calling reads as co-accessible (based on what criteria) and then overlapping with motifs?And 2) Similar to the analysis with CTCF/Tss, is the co-accessibility here occurring more likely than by chance (at least within cell type)?
In the abstract and discussion (line 378), they say "3DRAM-seq can be applied to any tissue" but they can't really claim this, they have profiled still a limited number of cell types.
In both their prior submission and the current one, the authors state that they have developed a novel variant of MPRAs.But they haven't really done this.I think it would be more appropriate to say something like that they have "developed novel systems applying MPRAs in organoids."Saying they have a novel variant of MPRAs is misleading to readers.They have this in the abstract and at line 276-277 and line 367.

Minor comments:
There is a competing preprint that has been online for quite some time now describing a somewhat similar method that I think the authors should cite: (https://www.biorxiv.org/content/10.1101/2022.03.29.486102v1).
Lhx2 in olfactory neurons is involved in the formation of long range and interchromosomal hubs.Are they seeing something similar or are these more local?Further Lhx2 is associated with Ldb proteins as a bridging molecule (and not CTCF/cohesin), so do the Lhx2 interactions show similar enrichment for CTCF/cohesin as non-Lhx2 interactions?This is an interesting observation.
In the prior submission, I suggested that the authors should show a sample that has not been treated with the GpC methyltransferase in Figure 1B and C. I still think they should do this.They point out that prior work showed that the GpC affects CpG methylation only in certain base contexts and they exclude these, and that is fine, but there is also an issue of the levels of non-CpG methylation in these cells and whether this is more likely to occur over accessible regions or not.I don't think they really need to do any additional experiments here, even just reanalyzing previously published bisulfite sequencing data from the same cell line would be sufficient.
For Fig. 2G, I think the authors should plot the total number of contacts in the datasets they use to make the figure here.The lack of signal in Methyl-HiC and enhanced signal in Hi-C could easily be due to read depth.Not accounting for read depth here could artificially lead readers to believe that certain methods are higher or lower quality when instead it could just be how much sequencing was performed.
For Fig. 2E, they say they define Hi-C contacts as paired end reads spanning a ligation junction, but I think that this isn't a great way to defined contacts, as if reads were simply undigested they would be counted as a contact according to this metric.I think that it is better to use distance cut offs that are larger than the fragment sized used for sequencing, in particular for defining "intra" contacts in order to exclude possibly uncut fragments from being counted as "contacts".This is a minor language point, but the authors write: "We then performed 3DRAM-seq in mESC in three biological replicates (Supplementary Data 2), which were characterized by high bisulfite conversion efficiency at both CpG and GpC dinucleotides (>98%, Extended Data Fig. 1C)".The data in Extended Data Fig. 1C are from lambda phage DNA, but the way the sentence is written it makes it seem like they have a method for calculating conversion efficiency from the mESC genome.I think the authors should point out that the conversion efficiencies are from lambda DNA spike ins.
At line 139 the authors have a call out for Extended Data Fig. 2H, but it looks like this should be Extended Data Figure 2i.The same goes for line 142, they call out Ex. Fig. 2i but it looks like that should be Ex.Data Fig. 2J.Related to this, they don't call out real Ext.Data Fig. 2H in the main text, but they do discuss this in the reviewer response letter (so as a reviewer I get it but I don't think a reader would).
Reviewer #2: Remarks to the Author: The study introduces a novel and the revision significantly expands the 3DRAM-seq approach, which simultaneously profiles three epigenetic layers (3D genome, chromatin accessibility, and DNA methylation) at a single-cell resolution.The authors benchmarked the method against other multiomic approaches and found that it had the highest proportion of uniquely mapped reads and total Hi-C contacts.They also demonstrated the method's applicability to human cortical organoids and investigated the roles of transposable elements and specific transcription factors in regulating enhancers in human neurogenesis.Overall, the study's findings provide new insights into the complex interplay between epigenetic mechanisms in gene regulation and highlight the importance of simultaneous profiling of multiple layers of epigenetic information.The 3DRAM-seq method represents a significant step forward in multi-omic analysis and has the potential to advance our understanding of epigenetic regulation in diverse biological systems.
The revision process has improved the manuscript and it should be accepted.
Reviewer #3: Remarks to the Author: I have carefully considered the rebuttal provided by the authors, and I am afraid the revision did not significantly change my assessment of the manuscript: the works are solid, but the novelty and impact do not reach NCB.The more fundamental concerns are -1.the technological novelty is moderate.The profiling and validation methods are similar to that presented in Noack et al., 2022, published in a journal with a similar impact (Nature Neuroscience). 2. The novelty of biological samples (cerebral organoids) is modest.The culture of cerebral organoids is a relatively standard technique.It is unclear how much the findings represent endogenous biology without a rigorous comparison with primary human brain tissue.

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---------Please don't hesitate to contact NCB@nature.com should you have queries about any of the above requirements ----------The average accessibility (based on GpC methylation) is calculated separately for read1 and read2 in a chosen window (100bp) centered at the motif of interest.Additionally, we required that the motifs are separated by at least 1kb on the linear genome.-The resulting 2 columns matrix is then used as input for k-means clustering with k=4, iter.max=10000,nstart=100.Clusters are then reordered based on their mean value for consistency and the matrix is plotted using the R package complex heatmap.All subsequent analysis is performed using the exact same cluster assignments.-To test if there is a dependency between accessibility in read1 and read2 we used fisher exact test on the 2x2 contingency matrix and we reported the odds ratio as well as the p-value (0.94 and 0.52 respectively for Ctcf pairs in Fig 3B).This approach is analogous to what was used in Sönmezer et al., Mol Cell 2021 to test for co-occupancy of TF using single-molecule GpC methylation data and aims to test whether the null hypothesis (accessibility at read1 and read2 are independent events) can be rejected.In the case of Ctcf and CRE-TSS the null hypothesis could not be rejected, therefore we concluded that the accessibility pattern at read1 and read2 is a result of independent events.Same results were obtained if we used a Chi-squared test instead (X-squared=0.38)or pearson's correlation (r = 0.0024) Related to above, for the "co-accessibility pattern" testing (line 170-171), it isn't clear at all how this analysis was performed.For example, if their randomized control is 5% and their observed is 19%, how do they perform the testing and get an odds ratio of 0.94?They need more details here on what was done.
We apologies for the confusion here.The randomized control is generated by including read pairs where read2 does not have to overlap with a Ctcf motif (in comparison to Fig 3B, where both reads have to overlap a Ctcf motif).This is useful to estimate the "genomic background" probability for a read to be accessible and to show that if a read overlaps a Ctcf motif it has a high probability of being accessible (~43% for read2 in Fig3B vs only 12% of read2 spanning a random position in Fig S3A).However, this randomized control cannot be used to test if co-accessibility at CTCF sites results from independent or synergistic events, because there is no requirement for read2 to overlap a CTCF site.This is why we use the fisher exact test as described above.
I think the authors should comment in the discussion on the fact they don't see greater than expected coaccessibility of CTCF or CRE-TSS during interactions.It is a somewhat unexpected result (at least in my mind) and I think providing some level of interpretation or speculation about why this is observed would be helpful.I think that this is an important result more broadly for the single-cell community, as methods for single-cell ATAC-seq for linking CRE to genes rely on such "co-accessibility" measurements (PMID 30078726), but this makes it clear that co-accessibility (at least within cell types) doesn't really match with other molecular features that relate to enhancer-promoter connectivity.
We agree that these results are important and elaborate more in the discussion (L277-283).We believe that these results are consistent with recent findings based on microscopy which suggest that Ctcf binding is highly dynamic and loops occur only between 3-6% of the time (Gabriele et al., Science 2022).Similarly, live imaging of enhancer-promoter contacts at the Sox2 locus (Alexander et al, eLife 2019) suggest that such loops are also relatively rare and are not directly correlated with transcription (although enhancerpromoter proximity influence gene expression in a non-linear way -Zuin et al., Nature 2022).Therefore, one hypothesis is that the time these regions spend in proximity may not be enough for synergistic (or antagonistic) effects on accessibility.A testable prediction from this hypothesis (however outside of the scope of this manuscript) would be that a forced chromatin loop between two Ctcf sites (or CRE-TSS) would increase the co-accessibility levels.
Regarding using co-accessibility to link CREs and genes in single-cell data, it is important to note that current algorithms (such as Cicero) aggregate accessibility across multiple (typically 50) cells to gain power, thus they do not represent truly single-cell measurements.Second, as the reviewer correctly points out, such co-accessibility links are usually performed in tissues containing different cell types.This is similar to our analysis in the organoids where we do indeed observe changes in co-accessibility across two cell types (Fig 5J -K).
Can the authors further explain the analysis in Figure 5J and K? This is a two part question, 1) Are they first just calling reads as co-accessible (based on what criteria) and then overlapping with motifs?And 2) Similar to the analysis with CTCF/Tss, is the co-accessibility here occurring more likely than by chance (at least within cell type)?
The analysis for Figure 5J and 5K was performed very similar to the CTCF-based analysis in Figure 3B.First, we filtered LHX2/SOX2 or NEUROG2/EOMES motifs retaining only those that overlapped with a GpC peak (based on bulk accessibility in RGC or IPC respectively).Next, we identified all read pairs where read1 overlapped with one of the motifs (for example LHX2) and read2 overlapped with the other motif (SOX2 respectively).We then measured the average accessibility per read within a 50bp window for reads that are separated by at least 100bp but not more than 300bp.This distance cutoff is different from our measurements of long-range interactions associated with Ctcf loops, because we wanted to determine if these pairs of TFs interact directly or co-bind on chromatin synergistically at closer distances.We now include also the co-accessibility statistics for both LHX2/SOX2 and NEUROG2/EOMES (L188-191).Fisher's exact test confirms that the two interact synergistically (odds ratios for SOX2-LHX2: 3.23 in RGC 9.3 in IPC; odds ratios for NEUROG2-EOMES: 11.08 in RGC and 4.1 in IPC).Interestingly, these results suggest that the low relative proportion of co-accessible reads in the opposite cell type (9 and 8% respectively) are likely also a result of synergistic interactions between TFs, probably expressed at low levels in either neurogenic RGC or not fully mature IPCs.
In the abstract and discussion (line 378), they say "3DRAM-seq can be applied to any tissue" but they can't really claim this, they have profiled still a limited number of cell types.
We agree with the reviewer and apologies with the overstatement.We have now removed this statement from the abstract and the discussion.
In both their prior submission and the current one, the authors state that they have developed a novel variant of MPRAs.But they haven't really done this.I think it would be more appropriate to say something like that they have "developed novel systems for applying MPRAs in organoids."Saying they have a novel variant of MPRAs is misleading to readers.They have this in the abstract and at line 276-277 and line 367.
We agree with the reviewer and had already adjusted the text in the revised version to replace "novel" with "modified".We believe that the reviewer was looking at the original version of the manuscript, as there is no mention of MPRA in the respective lines in the resubmitted version.Nevertheless, we have now adopted the reviewer's suggestion and have either removed this altogether (in the abstract) or referred to it as "developed a novel system for applying an MPRA" in the discussion (L27-28, L215-216, L299).

Minor comments:
There is a competing preprint that has been online for quite some time now describing a somewhat similar method that I think the authors should cite: (https://www.biorxiv.org/content/10.1101/2022.03.29.486102v1).
We now cite and discuss this manuscript and comment on the differences and similarities between NOMe-HIC and 3DRAM-seq (L302-309).
Lhx2 in olfactory neurons is involved in the formation of long range and interchromosomal hubs.Are they seeing something similar or are these more local?Further Lhx2 is associated with Ldb proteins as a bridging molecule (and not CTCF/cohesin), so do the Lhx2 interactions show similar enrichment for CTCF/cohesin as non-Lhx2 interactions?This is an interesting observation.
The analysis in Figure 5 focuses on intraTAD interactions between pairs of regions overlapping an accessible LHX2 motif.In that sense, these interactions are local and are different than the long-range and trans contacts reported by the Lomvardas lab in olfactory neurons.Indeed, we don't observe such longrange trans-interactions in our data, but this is not surprising as they have been reported in mouse and only form during the maturation of olfactory neurons.Instead, our results point to a role of LHX2 in mediating close-range interactions, potentially also via LDB1 (now included in the discussion -L288-290).
How mechanistically these contacts are established and if this is dependent of Ctcf/cohesin is a very interesting questions, which is however difficult to resolve currently, as there is no ChIP/Cut&Run data available in these cell types or tissues.
In the prior submission, I suggested that the authors should show a sample that has not been treated with the GpC methyltransferase in Figure 1B and C. I still think they should do this.They point out that prior work showed that the GpC affects CpG methylation only in certain base contexts and they exclude these, and that is fine, but there is also an issue of the levels of non-CpG methylation in these cells and whether this is more likely to occur over accessible regions or not.I don't think they really need to do any additional experiments here, even just reanalyzing previously published bisulfite sequencing data from the same cell line would be sufficient.
We agree with the reviewer and have now included a plot comparing the levels of GpC methylation between 3DRAM-seq and WGBS at Ctcf and Nrf1 sites (as in Fig 1B and 1C).
The results clearly show that there is amost no detectable GpC methylation and no preferential enrichment at accessible regions unless treated with the M.CviPI GpC methyltransferase.
For Fig. 2G, I think the authors should plot the total number of contacts in the datasets they use to make the figure here.The lack of signal in Methyl-HiC and enhanced signal in Hi-C could easily be due to read depth.Not accounting for read depth here could artificially lead readers to believe that certain methods are higher or lower quality when instead it could just be how much sequencing was performed.
We agree with the reviewer and have included this information in Figure 2F legend.Indeed, the reviewer is correct, and the resolution is highly correlated with the sequencing depth, which we now specifically state in the figure legend.Our intention was simply to show that 3DRAM-seq is capable of identifying close-range interactions similarly to the other methods.
For Fig. 2E, they say they define Hi-C contacts as paired end reads spanning a ligation junction, but I think that this isn't a great way to defined contacts, as if reads were simply undigested they would be counted as a contact according to this metric.I think that it is better to use distance cut offs that are larger than the fragment sized used for sequencing, in particular for defining "intra" contacts in order to exclude possibly uncut fragments from being counted as "contacts".
Indeed, the reviewer is correct and this is very important to consider in chromatin conformation capture data.All of our analysis has been done using contacts separated by at least 1kb on the linear genome to avoid precisely these potential "false positive" contacts.Previously we have included information about "short" and "long range" contacts in Supplementary Table 1, but we now explicitly add this also in Fig 2E to compare across all different methods.As expected, methods which utilize streptavidin-biotin to enrich for ligation events (3DRAM-seq, Methyl-HiC) lead to a higher percentage of informative, long-range interactions.
This is a minor language point, but the authors write: "We then performed 3DRAM-seq in mESC in three biological replicates (Supplementary Data 2), which were characterized by high bisulfite conversion efficiency at both CpG and GpC dinucleotides (>98%, Extended Data Fig. 1C)".The data in Extended Data Fig. 1C are from lambda phage DNA, but the way the sentence is written it makes it seem like they have a method for calculating conversion efficiency from the mESC genome.I think the authors should point out that the conversion efficiencies are from lambda DNA spike ins.
We thank the reviewer for pointing out this ambiguity and have amended it accordingly "…efficiency based on spike-in controls" (L70-71).
At line 139 the authors have a call out for Extended Data Fig. 2H, but it looks like this should be Extended Data Figure 2i.The same goes for line 142, they call out Ex. Fig. 2i but it looks like that should be Ex.Data Fig. 2J.Related to this, they don't call out the real Ext.Data Fig. 2H in the main text, but they do discuss this in the reviewer response letter (so as a reviewer I get it but I don't think a reader would).
We apologies for this oversight and have amended the text accordingly, including also a reference to Extended Data Fig. 2H. Reviewer#2: Remarks to the Author: The study introduces a novel and the revision significantly expands the 3DRAM-seq approach, which simultaneously profiles three epigenetic layers (3D genome, chromatin accessibility, and DNA methylation) at a single-cell resolution.The authors benchmarked the method against other multiomic approaches and found that it had the highest proportion of uniquely mapped reads and total Hi-C contacts.They also demonstrated the method's applicability to human cortical organoids and investigated the roles of transposable elements and specific transcription factors in regulating enhancers in human neurogenesis.Overall, the study's findings provide new insights into the complex interplay between epigenetic mechanisms in gene regulation and highlight the importance of simultaneous profiling of multiple layers of epigenetic information.The 3DRAM-seq method represents a significant step forward in multi-omic analysis and has the potential to advance our understanding of epigenetic regulation in diverse biological systems.
The revision process has improved the manuscript and it should be accepted.
We thank the reviewer for the helpful comments and insights, which have substantially improved the manuscript.
Reviewer #3: Remarks to the Author: I have carefully considered the rebuttal provided by the authors, and I am afraid the revision did not significantly change my assessment of the manuscript: the works are solid, but the novelty and impact do not reach NCB.
The more fundamental concerns are -1.the technological novelty is moderate.The profiling and validation methods are similar to that presented in Noack et al., 2022, published in a journal with a similar impact (Nature Neuroscience).
2. The novelty of biological samples (cerebral organoids) is modest.The culture of cerebral organoids is a relatively standard technique.It is unclear how much the findings represent endogenous biology without a rigorous comparison with primary human brain tissue.
We apologies for not making the conceptual novelty of our study clearer.We have now gone thoroughly through the text to ensure we highlight the technical novelty of the study and how it differs from previously published multiomic methods.Fruthermore, we believe that the comparison with the human fetal brain epigenetic landscape (L146-148) indicates that our cortical organoids are highly representative of the human brain tissue, at least at the level of chromatin accessibility.
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Figure R1 .
Figure R1.GpC methylation levels across Ctcf or Nrf1 bound motifs for either 3DRAM-seq or WGBS data from the same cell line (E14).
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