Specification of distinct cell types in a sensory-adhesive organ for metamorphosis in the Ciona larva
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Abstract
The papillae of tunicate larvae contribute sensory, adhesive, and metamorphosis-regulating functions that are crucial for the biphasic lifestyle of these marine, non-vertebrate chordates. We have identified additional molecular markers for at least five distinct cell types in the papillae of the model tunicate Ciona, allowing us to further study the development of these organs. Using tissue-specific CRISPR/Cas9-mediated mutagenesis and other molecular perturbations, we reveal the roles of key transcription factors and signaling pathways that are important for patterning the papilla territory into a highly organized array of different cell types and shapes. We further test the contributions of different transcription factors and cell types to the production of the adhesive glue that allows for larval attachment during settlement, and to the processes of tail retraction and body rotation during metamorphosis. With this study, we continue working towards connecting gene regulation to cellular functions that control the developmental transition between the motile larva and sessile adult of Ciona .
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In this impressive study, the authors use analysis of single-cell RNA-seq data to identify marker genes of the multiple celltypes found in the tunicate papillae. The authors build reporter genes and use combinatorial reporter expression to disambiguate the identity of cells within the papillae. The authors go further to use tissue-specific CRISPR perturbations to construct a genetic hierarchy of factors that determines many of the different cell identities within the papillae, and also functionally characterize cell types by assessing their ability to produce cement, characterizing the gene expression of knockouts using bulk RNA-Seq, and validating that observed gene expression changes (such as for the cytoskeletal gene villin) are functionally relevant for cell identity.
It's very impressive that the authors were able to build this …
In this impressive study, the authors use analysis of single-cell RNA-seq data to identify marker genes of the multiple celltypes found in the tunicate papillae. The authors build reporter genes and use combinatorial reporter expression to disambiguate the identity of cells within the papillae. The authors go further to use tissue-specific CRISPR perturbations to construct a genetic hierarchy of factors that determines many of the different cell identities within the papillae, and also functionally characterize cell types by assessing their ability to produce cement, characterizing the gene expression of knockouts using bulk RNA-Seq, and validating that observed gene expression changes (such as for the cytoskeletal gene villin) are functionally relevant for cell identity.
It's very impressive that the authors were able to build this many different reporter constructs and to perform a range of different tissue-specific CRISPR knockouts to map the differentiation hierarchy of this tissue with such depth.
I'm curious to know what genes are expressed in these individual cell types and how one could understand their evolution. Since sequence-based homology seems not to have been effective in identifying homologs to other organisms, it would be interesting if the authors could explore structure-based approaches or leverage cross-species analysis approaches that can be performed using existing scRNA-Seq data.
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Our identification of molecular signatures for both collocyte subtypes in the papillae of Ciona provides a starting point for future investigations.
If you took the markers you genetically described here and looked back at the scRNA-Seq data, could you identify the core genes specifically responsible for producing the cement? Based on figure 5, it seems that ICs and OCs both contribute to cement production.
You could also leverage existing single-cell RNA-seq datasets and see if you can integrate expression from multiple species. Software such as SAMap or Seurat's CCA algorithm might allow you to merge cells from different species to look for shared gene expression signatures.
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To show that Villin is required for proper morphogenesis of Islet+ cells in the papilla, we performed tissue-specific CRISPR knockout using a combination of three validated sgRNAs spanning most of the coding sequence (Supplemental Figure 3E).
It's really impressive that this kind of tissue-specific knockout in just a few cells works well enough to see a measurable effect. I imagine the knockout efficiency must be very high!
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This suggests relatively significant changes to the cis-regulatory sequences of these cell type-specific genes in these otherwise nearly indistinguishable cryptic species.
If you were to align the orthologous sequences, how different are they? Are there particular motifs that seem to be mutually enriched in those sequences?
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None of the selected genes showed any appreciable homology to genes of known function in other organisms
How was homology assessment determined? I can't seem to find mention of this in the methods.
I'm curious if functional analogs of these proteins could be identified using structural similarity approaches, such as Foldseek. The Foldseek web server provides a user-friendly GUI that could be used to search for similar proteins across the Alphafold (or other) databases and could provide a means to identify functionally relevant proteins in other organisms that aren't easily detected using homology approaches.
You could also try using HMMs to determine annotations for these proteins, if they're not available. HMMER has a web interface that searches existing HMM databases against your query sequence.
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https://github.com/katarzynampiekarz/ciona_gene_model_converter (Piekarz and Stolfi, under review
The programmatic version (noGUI) version of this tool could be made a little easier to use by accepting inputs from the command line, such as with argparse.
It should be somewhat straightforward to modify the code to make it accept command line arguments. A tutorial can be found here: https://realpython.com/command-line-interfaces-python-argparse/
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