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  1. Author Response:

    Reviewer #3 (Public Review):

    [...]Overall, the quality of the RNAseq data seems sound, and the conclusions presented seem mostly supported by the data. Additionally, the manuscript is well written and easy to read.

    We are thankful to hear that the quality of the paper convinces the reviewer.

    The spatial profiling of Physarum by physically segregating it by centrifuging it into a 384-well plate is clever. While the approach is probably cannot be generalized to most organisms, it still provides a nice example of creative experimental design that is somewhat lacking in the single-cell genomics field at the moment. Moreover, given that there seem to be no/few published studies with RNA in situ hybridization gene expression patterns in this animal, it probably provides a wealth of information to Physarum researchers.

    We are glad to hear that the reviewer finds our experimental design clever and we also believe that our manuscript is a great resource for the Physarum but potentially also the whole protozoan and fungi community.

    Some aspects of the experimental design potentially limit the conclusions that can be drawn from the data. The authors find that plasmodia in distinct states of life (mitotic, non-mitotic, chemotaxing, contacting food) have broad syncytium-wide transcriptional differences. A major caveat of this finding is each separate condition was only profiled once without replicates, which makes it more difficult to tie which of these transcriptional differences are related to the samples' biological differences and which might be a batch effect.

    We agree with the reviewer on that point and try in our revised manuscript to also highlight similarities between replicate samples (SM1/2) and similar plasmodium parts (fans) in Fig. 2- Fig. supp. 1D-F and not only the differences in order to show that there are also overlaps suggesting that differences are not driven by batch. However, some of our observations might have some effect due to batch, and we have been careful in the revised manuscript to point this out. We feel that the methods that we have established in this manuscript can be employed in higher throughput in the future in order to sample multiple environmental conditions across multiple batches. Before our work, it had been entirely unclear if there is transcriptional heterogeneity within the syncytium and this is one of our major findings, which we feel is a very robust, interesting, and important finding.

    Additionally, it's not clear why the authors profiled different timepoints via snRNAseq (1 week with oat flake) and spatial RNAseq (only a few hours with oat flakes) in their experiment to assess feeding behavior.

    We thank the reviewer for the comment. The reason for not having the same time point is indeed not ideal but happened for technical reasons. The main goal for the snRNA-seq data acquisition was to obtain a rough spatial separation of fan and network parts which we believe is important to link the nuclei data to the spatially resolved data as shown in Fig. 3 (see point below). In order to obtain enough material, we needed to grow the plasmodium longer, whereas we performed all the Spatial Transcriptomics experiments in a more condensed time frame to keep at least these experiments as comparable as possible.

    While the work identifies spatial heterogeneity and nuclear heterogeneity, they are not directly compared (how much of the nuclear heterogeneity is explained by spatial heterogeneity?) perhaps because different timepoints were used with the two approaches?

    We apologize that this point was not conveyed clearly in our manuscript. We do try to draw a direct link between the nuclear and the spatial heterogeneity in Fig. 3G where we correlate the transcriptomes of the clusters of SM4 with the clusters identified for the secondary plasmodium. Strikingly, the fan specific cluster in SM4 is highest correlated with the nuclei cluster that is strongly enriched with nuclei from the fan sample allowing to draw a direct link between the nuclei and the spatially resolved data. We rewrote the corresponding paragraph accordingly. This also highlights that age has obviously rather a minor impact for the comparison. However, we agree that there are technically more direct techniques available like measuring the mRNA in situ (e.g. SeqFish or OsmFish) which are, unfortunately, being far away from being routinely established in Physarum.

    Additionally, some aspects of the analysis seem to miss opportunities. A significant portion of the presentation of the gene expression results discovered by the authors is focused on the cell cycle, which seems less exciting than perhaps other biological phenomena related to structural specialization within different parts of the organism or related to its feeding and metabolic behaviors might be.

    We are thankful for the reviewer’s suggestions and added new panels to Fig. 2- Fig. supp. 1D-F that focus on similarities between and within samples to emphasize other biological phenomena like common transcriptomic fan signatures. In line with this, we performed additional GO enrichment analyses (Fig. 2- Fig. supp. 3 and Fig. 3-Fig. supp. 1) which, for instance, also highlight GO enrichments in dependence on nutrient interaction in addition to our findings regarding cell cycle progression in Physarum. We agree that there are a lot of very interesting biological phenomena that can be explored with Physarum, however, we also think that regulation of cell cycle within the syncytium and in free-living cells is also interesting and important.

    Also, while multiple classes of nuclei (stationary and mobile) are identified, it's unclear how those relate to the different transcriptional states identified through the snRNAseq.

    We thank the reviewer for making this point and we would also like to understand differences between stationary and mobile nuclei. We agree that the next step is to find ways how to specifically label, track and isolate moving/stuck nuclei in order to understand how the different classes of nuclei are linked to the transcriptomic differences. Unfortunately, this is not yet possible but has to be the goal for future research.

    Lastly, some aspects of the presentation detract from the work. Some of the results and discussion focus on 'coordinated intra-syncytial behaviors', and a major one of focus is that a wave of mitosis seems to proceed across the organism. However, to my knowledge, many syncytial systems (e.g. Xenopus or Drosophila embryos) exhibit synchronized mitosis, so I would have expected this to be the default state, rather than an exciting finding. Is this result unexpected? If so, it would be helpful if better contextualized. One aspect that would likely improve this manuscript would be to place it more firmly within the larger context of well-studied syncytial cells that exhibit specialization. For instance, a major example of a well-studied syncytium that exhibits spatial gene expression and nuclear specialization is the Drosophila embryo, which undergoes much of its early patterning while syncytial. Furthermore, muscle cells are typically syncytial, and some exciting recent studies have similarly used snRNAseq to observe heterogeneity and specialization of particular nuclei.

    We are thankful for that comment and contextualize this finding now more clearly in our Discussion. Briefly, synchronous cell division is not the default state in acellular slime molds and also not for similarly functioning systems like fungi hyphae. However, the reviewer is right that the synchronized division is not a new finding we made neither for Physarum nor for other syncytial systems in general. Our research, however, adds to this view that such a synchronization wave can be established over a large distance in Physarum (much larger than in ‘standard’ developmental biology systems like frogs and flies) while nuclei are in addition in a continuous shuttle flow which makes it a unique model system compared to the different nuclei states that are established in more rigid systems like muscles and embryos. In addition, a link between mode of nuclei division and plasmodial size and age exists but is not yet understood emphasizing its uniqueness as model system.

    Lastly, while not meant to diminish the contributions of this work, it does seem that given the diversity of syncytia that are well studied and exhibit nuclear specification, perhaps the title "Nuclei are mobile processors enabling specialization in a gigantic single-celled syncytium" oversells the results presented in this work.

    We thank the reviewer for this point and changed the title of the manuscript to avoid overselling and to more accurately reflect the research presented in the manuscript. The new title is: “Spatial transcriptomic and single-nucleus analysis reveals heterogeneity in a gigantic single-celled syncytium.” We agree that more work is needed to finally prove that mobile nuclei can be used to more quickly ‘seed’ new transcriptomic states in other plasmodial parts, thereby acting as mobile processors.

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  2. Evaluation Summary:

    Single celled organisms are assumed to be smaller, simpler, and less complex than multicellular organisms like animals. However, there are many examples of large single-celled protists - especially amoeba - that can be up to centimeters in size, and it remains unclear how they are able to achieve these sizes and differentiated regions like tissues in animals. Here, the authors provide evidence for variation in gene expression in the syncytial (multinucleate) large amoeba Physarum polycephalum. While primarily descriptive work, the authors are claiming a provocative mechanistic interpretation of the single cell gene expression results, but not yet supported by the current data. This study is neverhteless elegant and interesting regarding heterogeneity of gene expression patterns and thus specialization of functions within a syncytial organism.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

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  3. Reviewer #1 (Public Review):

    This manuscript describes spatial heterogeneity in transcriptional profiles in the syncytial amoeboid protist Physarum using single-cell scRNAseq. Physarum amoebae exists in single nucleated (haploid) and syncytial (multinucleated) life cycle stages, and the syncytial which can grow very large (centimeters) with both networked and front or fan morphologies. This begs the question of how single cells - often larger than some animals - can maintain and differentiate their varied functions and communicate with the environment. Therefore, the overall scientific question and rationale for the work is to better understand spatial gene regulation in large differentiated syncytial organisms that could have implications for multicellular tissue evolution and differentiation. The primary claims of the manuscript include that there exists spatially differentiated transcription in the nuclei of the Physarum syncytia and the authors thus conclude that nuclei are "mobile" processors facilitating specialized functions - essentially that the nuclei integrate spatial cues that result in the syncytium to locally change morphology and behavior. This primarily mechanistic conclusion - while provocative - is not yet adequately supported by the primarily descriptive or correlative experimental data presented in the manuscript.

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  4. Reviewer #2 (Public Review):

    Review of "Nuclei are mobile processors enabling specialization in a gigantic single- celled syncytium". Physarum polycephalum is a member of Myxogastria, within the Amoebozoa, which is a sister clade to Opisthokonta, which includes animals and fungi. P. polycephalum grows as a multinucleate syncytial plasmodium, while other members of Amoebozoa grow as single nucleate cells (Dictyostelium). In this study, the authors use a clever method to isolate sections of a diploid plasmodium of P. polycephalum by growing a single plasmodium over a 384 well grid and isolating RNA from each colonized grids by centrifuging the 384 well plate. A total of 4 genetically identical plasmodia were assayed, two of which were grown in a uniform environment and two of which were exposed to oat flakes. The authors show variability in gene expression patterns across a plasmodium, with clustering of gene expression patterns in certain areas of the plasmodium, for example the growth front (fan) versus the vein network. Additionally, each of the four plasmodia also showed a unique expression patterns. Single nuclear RNAseq was also performed to assess expression patterns differences between nuclei within the syncytium and again, clustering of expression profiles was observed. Expression patterns in syncytial plasmodia was also compared to expression profiles obtained from single celled haploid amoeba. As between plasmodia, expression patterns differences were observed between individual amoebae, particularly in cell cycle functions, in addition to the expression of amoebae specific genes. This study is quite elegant and the data supports heterogeneity of gene expression patterns and thus specialization within a syncytial multinucleate plasmodium.

    Some information regarding the genome and predicted genes in the P. polycephalum is needed in the introduction, strengths and deficiencies as a model to understand syncytial function. This would be useful for those readers not familiar with this organism. The expression of how many of the predicted genes in the genome (~48,000) were detected in the clusters?

    The functions of different parts of the plasmodium were often extrapolated based on expression of marker genes whose function has been presumably characterized in other organisms. How these marker genes were identified and justification could be made clearer.

    Functional category analysis of gene clusters would be helpful to know if particular functions are enriched.

    In Videos 1 and 2, the nuclei show very different sizes and morphologies. It would be useful to have additional discussion.

    Within the plasmodium, using single nucleus RNAseq, did the number of reads per nucleus vary in the plasmodium? Could some nuclei be quiescent and transcriptionally inactive (for example, those that are moving) versus others that are anchored, as in Video 1?

    For the supplemental datasets, it would be useful to have detailed information about the expression levels of genes in each cluster, along with the UniProt prediction and gene ID in Physarum.

    The discussion needs more evaluation of methods and results of data presented, for example, what does it mean that each plasmodium (in an identical environmental condition) shows differences in expression patterns, including different areas of the plasmodium?

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  5. Reviewer #3 (Public Review):

    Typically, multicellular organisms exhibit cell type specialization, where cells perform distinct roles and have distinct transcriptional states that generate their diversity. Physarum polycephalum is a large, syncytial plasmodium that, despite being only a single cell, still exhibits specialization over different regions or subdomains. Each large cell contains thousands of nuclei. This work by Tobias Gerber, Cristina Loureiro, Nico Schramma, et al. endeavored to find whether that subdomain specialization is supported by spatially localized transcriptional states, and whether heterogeneity in expression between nuclei accounts for those spatially localized transcriptional domains. To do this, they quantified nuclear distribution and mobility using DNA dyes and live imaging and confirmed that the individual networks contain thousands of nuclei, some of which are mobile and others are stationary, as had been anecdotally observed. They spatially profiled gene expression within individual Physarum plasmodia by dividing them in a grid pattern into 384 samples and using scRNAseq library preparation techniques. They found that overarching transcriptional differences in cultures that were dividing, chemotaxing towards a food source, or actively in contact with that food source. By comparing expression within cultures, across space, they found that Physarum progress through the cell cycle and express different cytoskeletal regulators in the network and fan portions, and when in contact with food, transcribe genes involved in nutrient uptake/breakdown specifically in regions in contact with the food source. They then used single-nuclei RNAseq to determine the transcriptional state of individual nuclei within these plasmodia. They found that nuclei were in different cell cycle states, and that some nuclei seemed to be specialized to produce genes involved in fruiting body formation. Finally, to determine whether the observed nuclear heterogeneity was related to earlier expression differences (i.e. developmentally maintained), the authors profiled single-nucleated Physarum amoebae. They found that amoeba have an expression program that is distinct from syncytial Physarum and exhibit transcriptional hetereogeneity that is related to their phase in the cell cycle.

    Overall, the quality of the RNAseq data seems sound, and the conclusions presented seem mostly supported by the data. Additionally, the manuscript is well written and easy to read.

    The spatial profiling of Physarum by physically segregating it by centrifuging it into a 384-well plate is clever. While the approach is probably cannot be generalized to most organisms, it still provides a nice example of creative experimental design that is somewhat lacking in the single-cell genomics field at the moment. Moreover, given that there seem to be no/few published studies with RNA in situ hybridization gene expression patterns in this animal, it probably provides a wealth of information to Physarum researchers.

    Some aspects of the experimental design potentially limit the conclusions that can be drawn from the data. The authors find that plasmodia in distinct states of life (mitotic, non-mitotic, chemotaxing, contacting food) have broad syncytium-wide transcriptional differences. A major caveat of this finding is each separate condition was only profiled once without replicates, which makes it more difficult to tie which of these transcriptional differences are related to the samples' biological differences and which might be a batch effect. Additionally, it's not clear why the authors profiled different timepoints via snRNAseq (1 week with oat flake) and spatial RNAseq (only a few hours with oat flakes) in their experiment to assess feeding behavior. While the work identifies spatial heterogeneity and nuclear heterogeneity, they are not directly compared (how much of the nuclear heterogeneity is explained by spatial heterogeneity?) perhaps because different timepoints were used with the two approaches?

    Additionally, some aspects of the analysis seem to miss opportunities. A significant portion of the presentation of the gene expression results discovered by the authors is focused on the cell cycle, which seems less exciting than perhaps other biological phenomena related to structural specialization within different parts of the organism or related to its feeding and metabolic behaviors might be. Also, while multiple classes of nuclei (stationary and mobile) are identified, it's unclear how those relate to the different transcriptional states identified through the snRNAseq.

    Lastly, some aspects of the presentation detract from the work. Some of the results and discussion focus on 'coordinated intra-syncytial behaviors', and a major one of focus is that a wave of mitosis seems to proceed across the organism. However, to my knowledge, many syncytial systems (e.g. Xenopus or Drosophila embryos) exhibit synchronized mitosis, so I would have expected this to be the default state, rather than an exciting finding. Is this result unexpected? If so, it would be helpful if better contextualized. One aspect that would likely improve this manuscript would be to place it more firmly within the larger context of well-studied syncytial cells that exhibit specialization. For instance, a major example of a well-studied syncytium that exhibits spatial gene expression and nuclear specialization is the Drosophila embryo, which undergoes much of its early patterning while syncytial. Furthermore, muscle cells are typically syncytial, and some exciting recent studies have similarly used snRNAseq to observe heterogeneity and specialization of particular nuclei. Lastly, while not meant to diminish the contributions of this work, it does seem that given the diversity of syncytia that are well studied and exhibit nuclear specification, perhaps the title "Nuclei are mobile processors enabling specialization in a gigantic single-celled syncytium" oversells the results presented in this work.

    Read the original source
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