Multi-omics analysis identifies LBX1 and NHLH1 as central regulators of human midbrain dopaminergic neuron differentiation

  1. Borja Gomez Ramos
  2. Jochen Ohnmacht
  3. Nikola de Lange
  4. Aurélien Ginolhac
  5. Elena Valceschini
  6. Aleksandar Rakovic
  7. Rashi Halder
  8. François Massart
  9. Christine Klein
  10. Roland Krause
  11. Marcel H. Schulz
  12. Thomas Sauter
  13. Rejko Krüger
  14. Lasse Sinkkonen

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Midbrain dopaminergic neurons (mDANs) control voluntary movement, cognition, and reward behavior under physiological conditions and are implicated in human diseases such as Parkinson’s disease (PD). Many transcription factors (TFs) controlling human mDAN differentiation during development have been described, but much of the regulatory landscape remains undefined. Using a tyrosine hydroxylase (TH) iPSC reporter line, we have generated time series transcriptomic and epigenomic profiles of purified mDANs during differentiation. Integrative analysis predicted novel central regulators of mDAN differentiation and super-enhancers were used to prioritize key TFs. We find LBX1, NHLH1 and NR2F1/2 to be necessary for mDAN differentiation and show that overexpression of either LBX1 or NHLH1 can also improve mDAN specification. NHLH1 is necessary for the induction of neuronal miR-124, while LBX1 regulates cholesterol biosynthesis, possibly through mTOR signaling. Consistently, rapamycin treatment led to an inhibition of mDAN differentiation. Thus, our work reveals novel regulators of human mDAN differentiation.

Article activity feed

  1. Review Commons

    Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

    Learn more at Review Commons

    Reply to the reviewers

    Manuscript number: RC-2023-01862

    Corresponding author(s): Lasse, Sinkkonen

    1. General Statements [optional]

    This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

    In our manuscript we have aimed to take an unbiased and data-driven high-throughput approach for identification of transcription factors important for dopaminergic neuron differentiation via repeated, combined transcriptomics and epigenomics measurements. We also provide the research community with an extensive dataset enabling further studies on dopaminergic neurons beyond the scope of a single manuscript. We validate identified transcription factors not previously recognized being involved in mDAN differentiation. While we believe our approach is powerful in unbiased identification of central regulators, it does not focus only on factors that are unique for dopaminergic neurons. Importantly, the ranking of transcription factors is based on the epigenomic data of the target genes, rather than expression of transcription factors themselves. We have aimed for the genome-wide identification of pathways controlled by the identified transcription factors, for example through transcriptome analysis.

    For practical reasons, to gain the sufficient depth of data to accomplish our aim, only one iPSC line was used for the initial data generation. However, we fully agree on the need for validation of the key findings and overall gene expression profiles in additional independent cell lines. Please find below our detailed point-by-point plan on addressing the reviewers’ comments.

    2. Description of the planned revisions

    Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

    Reviewer #1 (Evidence, reproducibility and clarity (Required)):

    In this study, Ramos and colleagues defined gene regulatory networks and transcriptional landscape during differentiation of a human iPSC reporter line into dopaminergic neurons. Several omic techniques (RNA-seq, ATAC-seq, chromatin-IP) and modelling (EPIC-DREAM) allowed them to identify putative effectors of dopaminergic differentiation LBX1, NHLH1 and NR2F1/2. Using overexpression and shRNA-mediated knock down experiments, the authors attempted to validate the hits.

    This manuscript is very difficult to read and is confusing. The data are interesting, but they need to be presented in a more concise and readable way, in addition to be validated using additional iPSC lines. Below are few comments.

    We thank Reviewer1 for taking the time to evaluate our manuscript and for providing valuable feedback towards improving it further. We were happy to read that the Reviewer1 found the study interesting with only a few caveats. Here we will outline a detailed plan to address those limitations.

    In the revised manuscript we will do our best to improve the readability of the manuscript. However, since Reviewer2 has found that the manuscript is “well written, the research laid out in a clear way, and the experiments well thought”, it is somewhat difficult for us to identify the exact changes to introduce. Perhaps these are related to field-specific vocabulary or methodology, which we will aim to make more readable for broader audience.

    We agree on the concern of Reviewer1 that different human iPSC lines can show significant variability due to their individual genetic backgrounds. We have observed differences in the rate of neuronal differentiation, depending on the iPSC line, and transcriptomic analysis reveals hundreds of differentially expressed genes between independent iPSC lines. Still, in a case of a single healthy donor, we don’t expect an intra-individual variability to alter conclusions regarding key regulators of fundamental processes such as differentiation. To carry out our multi-omic analysis in the sufficient depth that we have applied and using only purified dopaminergic neurons with a TH-mCherry-reporter inserted using genome editing, it was (also budget-wise) not considered to include multiple independent iPSC lines for the entire panel of experiments (as our ambition was not to characterize a specific mutation). However, to address this point, we have generated a second iPSC line from a healthy donor with TH-mCherry-reporter inserted through genome editing. To address the concerns regarding variability between different human iPSC lines we plan to:

    1. Perform transcriptomic profiling of the second TH-mCherry-reporter line at selected time points of dopaminergic neuron differentiation to confirm the similarity of changes in cell identity at transcriptome level.

    2. Perform TH staining upon LBX1 or NHLH1 knock-down in additional iPSC lines following dopaminergic neuron differentiation, to confirm their effect on differentiation across iPSC lines. To do this we will apply the Yokogawa high content image analysis that has been recently established in our laboratories. This will also be related to the next point regarding microscopy images of the dopaminergic neuron differentiation and the effect of transcription factors on this.

    Only relative numbers and mRNA level normalized to control are presented in main figures. This is very confusing because there is no real quantification. Images of cultures to show increased/decreased number of dopaminergic neurons in non-FACS purified cultures following overexpression/knock down should be presented in main figures. It is recommended to add absolute quantification (percent of DAPI) and statistical analysis based on N=3 independent experiments.

    Thank you for raising this point. We are happy to clarify the quantification of dopaminergic neuron numbers and mRNA levels. All quantifications of dopaminergic neuron numbers were based on the mCherry reporter inserted in the TH locus through genome editing and expressed together with endogenous TH. While mCherry can be detected using microscopy (as shown in Figure 1), the signal is significantly weaker than what can be achieved through antibody staining and quantitative analysis is therefore much more accurate when systematically performed using FACS analysis and controlled by using a cell line without the mCherry reporter. Moreover, the approach is direct and not dependent on antibody specificity. Therefore, all quantifications of dopaminergic neuron numbers in the manuscript were performed using FACS.

    Most *in vitro *cell differentiation protocols show variability in their efficiency between independent experiments, which is typically reflected as variable expression levels of the different marker genes (Grancharova et al. 2021). This is also true for dopaminergic neuron differentiation and in our experiments the number of obtained dopaminergic neurons can vary between 5-20% while differentiations performed in parallel as part of the same experiment are typically very similar. Summarizing absolute numbers between independent experiments can lead to large variation while the relative effect of perturbation is reproducible. Therefore, our results are presented as relative changes in dopaminergic neuron numbers and mRNA levels.

    Nevertheless, to increase confidence in the impact of NHLH1 and LBX1 on dopaminergic neuron differentiation, we propose, as already described above, to perform TH staining upon LBX1 or NHLH1 knock-down in additional iPSC lines following dopaminergic neuron differentiation. To visualize the observed impact on differentiation.

    Based on images shown in figure S4, the effect of rapamycin is very low (no quantification is presented).

    We apologize for the unclear Figure Legend for Figure S4 that did not specify what is visualized in the image. The images represent the transduction efficiency of the neurons based on the GFP reporter co-expressed with the short hairpin constructs. The mCherry levels, that are quantified in Figure 6G, are not visualized in these images. We will correct the Figure Legend accordingly. As mentioned in the last sentence on page 20 of the manuscript (referring to Supplementary Figure S4), rapamycin did not induce similar level of reduction in cell numbers as LBX1 knock-down alone did.

    Are the three hits altered in dopaminergic neurons in Parkinson's disease and other synucleinopathies that could explain dysfunction of dopamine neurons in disease? Nurr1, EN1 and many other genes required for differentiation of dopaminergic neurons from pluripotent stem cells have their expression decreased in Parkinson's. It is expected that the expression of LBX1, NHLH1 and NR2F1/2 would change under disease condition.

    We have investigated the expression of LBX1, NHLH1 and NR2F1/2 using recent meta-analysis of post-mortem brain tissue transcriptomes of Parkinson’s disease patients (Tranchevent, Halder, & Glaab, 2023). None of these TFs was found to be dysregulated in Parkinson’s disease patients. This is consistent with the fact that the expression of these factors is not restricted only to A9 midbrain dopaminergic neurons that are primarily degenerating in Parkinson’s disease but can be detected also in several other types of neurons (please see also our response in section 4).

    However, NHLH1 expression is reduced in dopaminergic neurons derived from iPSCs of Parkinson’s disease patients carrying a LRRK2-G2019S mutation based on our published single cell RNA-seq data (Walter et al., 2021).

    Beyond this, our results implicate NHLH1 in the regulation of miR-124, which in turn has been found to be downregulated in Parkinson’s disease patients and neuroprotective in different animal models of Parkinson’s disease (Angelopoulou, Paudel, & Piperi, 2019; Saraiva, Paiva, Santos, Ferreira, & Bernardino, 2016; Yang, Li, Yang, Guo, & Li, 2021; Zhang et al., 2022). Similarly, a recent analysis of single nuclei RNA-seq of midbrains from Parkinson’s disease patients, showed that targets of NR2F2 were enriched in the vulnerable dopaminergic neuron population, promoting neurodegeneration (Kamath et al., 2022). Indicating the involvement of the pathway in disease progression without a change in transcription factor expression.

    Finally, a polymorphism in NHLH1 locus (rs2147472) is associated with schizophrenia while a polymorphism in LBX1 locus (rs12242050) is associated with Parkinson's disease, suggesting further involvement of these genes in disease risk.

    We propose to include these findings in the revised manuscript and discuss them in the context of the current literature.

    __Reviewer #1 (Significance (Required)): __

    Interesting study that needs to be replicated using additional cell lines.

    We would like to thank the reviewer for this positive conclusion and plan to address the key concerns using additional iPSC lines for transcriptome profiling and knock-down experiments.

    __ Reviewer #2 (Evidence, reproducibility and clarity (Required)):__

    In this manuscript Ramos et al. present a novel and comprehensive transcriptomic and epigenomic profile that identifies a series of key regulators of mDANs differentiation, providing functional validation and characterization of two newly associated TFs: LBX1 or NHLH1. In order to discover key regulators of mDAN differentiation the authors use their previous EPIC-DREM pipeline together with ATAC-seq data for the first time. Then, they focus their attention on those TFs with a more probable regulatory role by performing low input ChIP-seq for H3K27ac leading to the identification of 6 TFs as novel candidate regulators of mDAN differentiation under the control of super-enhancers at day 30 and day 50 of differentiation. In vitro knock down and overexpression of candidate TFs revealed LBX1, NHLH1 as important regulators of DAn differentiation. The authors then interrogate the role of these two TFs through RNA-seq and an Ingenuity Pathway Analysis (IPA)/g:Profiler and proposed regulation of the mature form miR-124 and cholesterol biosynthesis-related genes as the main processes controlled by NHLH1 and LBX1, respectively.

    Overall, the manuscript is well written, the research laid out in a clear way, and the experiments well thought. The novelty of this study lays in the combination of epigenomic and transcriptomic data at different time points in specific cells during DAn differentiation. I believe the conclusions are supported by the results presented and therefore recommend this paper for publication after addressing some minor points listed below:

    We would like to thank the reviewer for the detailed and overall positive evaluation of our work. We are grateful for the suggestions for improvements and below we detail our plan for addressing them.

    Minor comments:

    In page 15 the authors state "the list of 17 TFs was further explored to select the most promising candidates for functional analysis". However, they only named TCF4 and MEIS1 as examples of discarded TFs through literature search. It is not clear which of the remaining 15 TFs were discarded because of a literature search and which were by SE signal cutoff. Clarification is needed.

    We will add clarification statements here, providing more evidence for the selection of our candidates. For that, we will add a supplementary figure showing the locus and expression of the 11 TFs that were not selected.

    In page 15 the authors state "TFs, HOXB2, LBX1, NHLH1, NR2F1 (also known as COUP-TFI), NR2F2 (also known as COUP-TFII) and SOX4 were found to present the strongest SE signals and most dynamic gene expression profiles" however I could not find the data that corroborate this statement within tables or figures. Authors should provide hard data to support this statement.

    With the clarification from point 1, point 2 will also be answered for a clear description and criteria of our selection.

    In supplementary 3, in the IPA analysis some data appear with the warning "#¡NUM!" at the z-score. Some explanation should be given and if pertinent, added to the table legend.

    Sorry for not clarifying that in the dataset. That term is produced when IPA cannot predict the Z-score and it is represented in our bar graphs in grey (see Figure 6B). We will add that information to the table header.

    In methodology, some reagents and techniques appear with a code reference to catalog number and others don´t. Please keep it uniform throughout the text.

    In this study, we performed most of the techniques using kits which contained all necessary reagents for it. We will better clarify which reagents were provided by the manufacturer and which ones were additional to the kits.

    Supplementary table 1 has some TFs highlighted in yellow but there is no legend that explain what the yellow highlight symbolizes. Clarification is needed

    This is an error and there should not be any TF highlighted in yellow. We apologize for the inconvenience. The highlights will be removed from the revised tables.

    Format suggestions:

    For an easier to follow flow between figure 3A and the main text, it would be helpful if NR2F1 and NR2F2 graphs in Figure 3A appeared next to each other or one above the other.

    We will follow the recommendation from the reviewer, and we will change the order of the TFs in the figure to have both NR2F TFs next to each other.

    Supplementary table 2.

    Data is presented in a confused way. For example, the Top20_TFs_EPIC-DREM is presented as a list of names without divisions of type node or scoring annotations. It would be more informative and easy to follow if proper labeling and scoring is given within this spreadsheet without the necessity of navigating sup.table1 in parallel.

    it would be preferable to have an extra sheet showing the comparison between both data sets (SE and EPICDREAM) before providing a final list of relevant TFs.

    To the existing table containing the TFs controlled by SE, we are going to add the information regarding EPIC-DREM, namely, the rank those TFs got in each node together with their median ranking, and their best rank across nodes. That will give a good overview on how they look in both analyses. With this approach, some of the TFs controlled by SE will have no information regarding EPIC-DREM because their motif is not known according to the Jaspar database.

    Reviewer #2 (Significance (Required)):

    -General assessment:

    Overall, the manuscript is well written, the research laid out in a clear way, and the experiments well thought. The novelty of this study lays in the combination of epigenomic and transcriptomic data at different time points in specific cells during DAn differentiation and description of new roles in DAn differentiation for two TF: LBX1 or NHLH1.

    We thank the Reviewer2 for this assessment.

    -Limitations: One limitation, than the authors themselves mentioned, is the possibility that promising candidate TFs involved in mDAn differentiation are discarded or not taken in account by the EPIC-DREAM algorithm.

    We agree with this limitation and will make all the data available for the larger research community to use for follow-up work. Importantly, the data can be easily used for re-analysis using the same pipeline when improved databases become available.

    -Audience: This manuscript focuses on factors involved in mDAN differentiation which targets a highly specific audience however their multiomic and functional methodology might attract broader audiences looking to apply similar pipelines and/or experimentation in different areas of research.

    -My field of expertise:

    I am a geneticist and neuroscientist with expertise in molecular biology and epigenomics focused on age related neurodegenerative disorders.

    -Recommendation:

    I believe the conclusions are supported by the results presented and therefore recommend this paper for publication after addressing some minor points.

    3. Description of the revisions that have already been incorporated in the transferred manuscript

    Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

    No changes were introduced so far.

    4. Description of analyses that authors prefer not to carry out

    Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

    Below we will provide a point-by-point response to concerns raised by the reviewers that we believe are outside of the scope of our study.

    There is not data showing that the targets are specifically required for dopaminergic differentiation. One may argue that same targets may be identified and required for differentiation of other neuronal cell types. Hence, hits need to be validated for other neuronal cell types using knock in and shRNA mediated KO.

    The novelty of this study resides in the use of epigenomic signatures to predict TF activity across differentiation and couple those predictions with the transcriptional changes occurring during this process to identify the TFs responsible for most of the transcriptional changes observed. Therefore, although our focus were TFs important for establishing cell identity, we did not select TFs with a selective/exclusive expression in these cells, namely, cell identity TFs. This gives another perspective regarding TF activity and their relevance for cellular processes like differentiation.

    We believe the TFs identified in our study are likely to be involved in regulation in several other neuronal subtypes. There is a wide range of neuronal subtypes and selection and establishment of some of the those for testing of our factors seems biased but also outside of the scope of this study.

    Are the neurons generated following overexpression/shRNA-mediated knock down of the three hits functional? Electrophysiological recordings could help.

    What other functions are affected in dopaminergic neurons when targets are knocked-down? Is lysosomal activity changed? Is the level of synaptic proteins altered compared to control?

    In order not to bias our approach towards particular phenotypes by selected analysis such as electrophysiological measurements or lysosomal activity assays, we performed a transcriptional profiling upon TF depletion for our selected candidates. Our transcriptional profiling highlighted the main pathways affected by the TFs and they are presented and discussed in our study. We exploit these data to find the processes controlled by our TFs that help to define dopaminergic neuron cell identity. We discussed them and tested the role of mTOR signaling and miR-124 as targets of our TFs. The results from the RNA-seq analysis did not indicate direct regulation of synaptic or lysosomal activity, and therefore we find such analysis to be outside of the scope of our study.

    Moreover, since the knock-down of our candidate TFs is in general inhibiting dopaminergic differentiation, studying the dopaminergic neurons remaining after a knock-down risks focusing on cells that have either partially or completely escaped the knock-down. Thereby influencing the value of detailed analysis of their functionality.

    References:

    Grancharova, T., Gerbin, K.A., Rosenberg, A.B. et al. A comprehensive analysis of gene expression changes in a high replicate and open-source dataset of differentiating hiPSC-derived cardiomyocytes. Sci Rep 11, 15845 (2021). https://doi.org/10.1038/s41598-021-94732-1

    Angelopoulou, E., Paudel, Y. N., & Piperi, C. (2019). miR-124 and Parkinson’s disease: A biomarker with therapeutic potential. Pharmacological Research, 150. https://doi.org/10.1016/J.PHRS.2019.104515

    Kamath, T., Abdulraouf, A., Burris, S. J., Langlieb, J., Gazestani, V., Nadaf, N. M., … Macosko, E. Z. (2022). Single-cell genomic profiling of human dopamine neurons identifies a population that selectively degenerates in Parkinson’s disease. Nature Neuroscience, 25(5), 588–595. https://doi.org/10.1038/S41593-022-01061-1

    Saraiva, C., Paiva, J., Santos, T., Ferreira, L., & Bernardino, L. (2016). MicroRNA-124 loaded nanoparticles enhance brain repair in Parkinson’s disease. Journal of Controlled Release : Official Journal of the Controlled Release Society, 235, 291–305. https://doi.org/10.1016/J.JCONREL.2016.06.005

    Tranchevent, L. C., Halder, R., & Glaab, E. (2023). Systems level analysis of sex-dependent gene expression changes in Parkinson’s disease. Npj Parkinson’s Disease 2023 9:1, 9(1), 1–16. https://doi.org/10.1038/s41531-023-00446-8

    Walter, J., Bolognin, S., Poovathingal, S. K., Magni, S., Gérard, D., Antony, P. M. A., … Schwamborn, J. C. (2021). The Parkinson’s-disease-associated mutation LRRK2-G2019S alters dopaminergic differentiation dynamics via NR2F1. Cell Reports, 37(3). https://doi.org/10.1016/J.CELREP.2021.109864

    Yang, Y., Li, Y., Yang, H., Guo, J., & Li, N. (2021). Circulating MicroRNAs and Long Non-coding RNAs as Potential Diagnostic Biomarkers for Parkinson’s Disease. Frontiers in Molecular Neuroscience, 14, 28. https://doi.org/10.3389/FNMOL.2021.631553/BIBTEX

    Zhang, F., Yao, Y., Miao, N., Wang, N., Xu, X., & Yang, C. (2022). Neuroprotective effects of microRNA 124 in Parkinson’s disease mice. Archives of Gerontology and Geriatrics, 99. https://doi.org/10.1016/J.ARCHGER.2021.104588

  2. Review Commons

    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 #2

    Evidence, reproducibility and clarity

    In this manuscript Ramos et al. present a novel and comprehensive transcriptomic and epigenomic profile that identifies a series of key regulators of mDANs differentiation, providing functional validation and characterization of two newly associated TFs: LBX1 or NHLH1. In order to discover key regulators of mDAN differentiation the authors use their previous EPIC-DREM pipeline together with ATAC-seq data for the first time. Then, they focus their attention on those TFs with a more probable regulatory role by performing low input ChIP-seq for H3K27ac leading to the identification of 6 TFs as novel candidate regulators of mDAN differentiation under the control of super-enhancers at day 30 and day 50 of differentiation. In vitro knock down and overexpression of candidate TFs revealed LBX1, NHLH1 as important regulators of DAn differentiation. The authors then interrogate the role of these two TFs through RNA-seq and an Ingenuity Pathway Analysis (IPA)/g:Profiler and proposed regulation of the mature form miR-124 and cholesterol biosynthesis-related genes as the main processes controlled by NHLH1 and LBX1, respectively. Overall, the manuscript is well written, the research laid out in a clear way, and the experiments well thought. The novelty of this study lays in the combination of epigenomic and transcriptomic data at different time points in specific cells during DAn differentiation. I believe the conclusions are supported by the results presented and therefore recommend this paper for publication after addressing some minor points listed below:

    Minor comments:

    1. In page 15 the authors state "the list of 17 TFs was further explored to select the most promising candidates for functional analysis". However, they only named TCF4 and MEIS1 as examples of discarded TFs through literature search. It is not clear which of the remaining 15 TFs were discarded because of a literature search and which were by SE signal cutoff. Clarification is needed.
    2. In page 15 the authors state "TFs, HOXB2, LBX1, NHLH1, NR2F1 (also known as COUP-TFI), NR2F2 (also known as COUP-TFII) and SOX4 were found to present the strongest SE signals and most dynamic gene expression profiles" however I could not find the data that corroborate this statement within tables or figures. Authors should provide hard data to support this statement.
    3. In supplementary 3, in the IPA analysis some data appear with the warning "#¡NUM!" at the z-score. Some explanation should be given and if pertinent, added to the table legend.
    4. In methodology, some reagents and techniques appear with a code reference to catalog number and others don´t. Please keep it uniform throughout the text.
    5. Supplementary table 1 has some TFs highlighted in yellow but there is no legend that explain what the yellow highlight symbolizes. Clarification is needed

    Format suggestions:

    1. For an easier to follow flow between figure 3A and the main text, it would be helpful if NR2F1 and NR2F2 graphs in Figure 3A appeared next to each other or one above the other.
    2. Supplementary table 2.
      • a. Data is presented in a confused way. For example, the Top20_TFs_EPIC-DREM is presented as a list of names without divisions of type node or scoring annotations. It would be more informative and easy to follow if proper labeling and scoring is given within this spreadsheet without the necessity of navigating sup.table1 in parallel.
      • b. it would be preferable to have an extra sheet showing the comparison between both data sets (SE and EPICDREAM) before providing a final list of relevant TFs.

    Significance

    General assessment:

    Overall, the manuscript is well written, the research laid out in a clear way, and the experiments well thought. The novelty of this study lays in the combination of epigenomic and transcriptomic data at different time points in specific cells during DAn differentiation and description of new roles in DAn differentiation for two TF: LBX1 or NHLH1.

    Limitations:

    One limitation, than the authors themselves mentioned, is the possibility that promising candidate TFs involved in mDAn differentiation are discarded or not taken in account by the EPIC-DREAM algorithm.

    Audience:

    This manuscript focuses on factors involved in mDAN differentiation which targets a highly specific audience however their multiomic and functional methodology might attract broader audiences looking to apply similar pipelines and/or experimentation in different areas of research.

    My field of expertise:

    I am a geneticist and neuroscientist with expertise in molecular biology and epigenomics focused on age related neurodegenerative disorders.

    Recommendation:

    I believe the conclusions are supported by the results presented and therefore recommend this paper for publication after addressing some minor points.

  3. Review Commons

    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

    In this study, Ramos and colleagues defined gene regulatory networks and transcriptional landscape during differentiation of a human iPSC reporter line into dopaminergic neurons. Several omic techniques (RNA-seq, ATAC-seq, chromatin-IP) and modelling (EPIC-DREAM) allowed them to identify putative effectors of dopaminergic differentiation LBX1, NHLH1 and NR2F1/2. Using overexpression and shRNA-mediated knock down experiments, the authors attempted to validate the hits.

    This manuscript is very difficult to read and is confusing. The data are interesting, but they need to be presented in a more concise and readable way, in addition to be validated using additional iPSC lines. Below are few comments.

    Only relative numbers and mRNA level normalized to control are presented in main figures. This is very confusing because there is no real quantification. Images of cultures to show increased/decreased number of dopaminergic neurons in non-FACS purified cultures following overexpression/knock down should be presented in main figures. It is recommended to add absolute quantification (percent of DAPI) and statistical analysis based on N=3 independent experiments.

    There is not data showing that the targets are specifically required for dopaminergic differentiation. One may argue that same targets may be identified and required for differentiation of other neuronal cell types. Hence, hits need to be validated for other neuronal cell types using knock in and shRNA mediated KO.

    Based on images shown in figure S4, the effect of rapamycin is very low (no quantification is presented).

    Are the neurons generated following overexpression/shRNA-mediated knock down of the three hits functional? Electrophysiological recordings could help.

    What other functions are affected in dopaminergic neurons when targets are knocked-down? Is lysosomal activity changed? Is the level of synaptic proteins altered compared to control?

    Are the three hits altered in dopaminergic neurons in Parkinson's disease and other synucleinopathies that could explain dysfunction of dopamine neurons in disease? Nurr1, EN1 and many other genes required for differentiation of dopaminergic neurons from pluripotent stem cells have their expression decreased in Parkinson's. It is expected that the expression of LBX1, NHLH1 and NR2F1/2 would change under disease condition.

    Significance

    Interesting study that needs to be replicated using additional cell lines.

  4. Version published to 10.1101/2023.01.27.525898v1 on bioRxiv