Single Cell Transcriptome Defines Cell Type Repertoire of Adult Daphnia magna.
Krishnan, I.; Yampolsky, L. Y.; Petrova, K.; Peshkin, L.
Show abstract
Detailed knowledge of transcriptional responses to environmental and developmental cues is impossible without single cell (SC) resolution data. We performed two SC RNAseq experiments surveying transcriptional profiles of females and males of D. magna, a freshwater plankton crustacean which is both a classic and emerging new model for eco-physiology, toxicology, and evolutionary genomics. We were able to identify over 30 distinct cell types about half of which could be functionally annotated. First, we identified ovaries- and testis-related cell types by focusing on female- and male-specific clusters. Second, we compared markers between SC clusters and bulk RNAseq data on transcriptional profiles of early embryos, circulating hemocytes, midgut, heads (containing brain, eyes, muscles and hepatic caeca), antennae II, and carapace. Finally, we compared transcriptional profiles of Daphnia cell clusters with orthologous markers of 250+ cell types annotated in Drosophila cell atlas. This allowed us to recognize striated muscle cells, gut enterocytes, cuticular cells, as well as 5 different neuron types, including photoreceptors and 3 ovaries-related clusters, one of which tentatively identified as the germ line cells. One well-defined cluster showed a significant enrichment in markers of both hemocytes and fat body of Drosophila, but not with bulk RNAseq data from circulating hemocytes, allowing us to hypothesize the existence of non-circulating, fat body-associated population of hemocytes in Daphnia. On the other hand, the circulating hemocytes express numerous cuticular proteins suggesting their role, in addition to macrophagy, in wound repair. At the same time numerous cell types remain unidentified, including those that map to FCA groups ambiguously or are characterized by Daphnia-specific markers with no clear orthology in the fruitfly. Likewise, many known or presumed cell types or tissues in Daphnia have not been identified to SC clusters. A detailed in-situ hybridization study would be necessary to match not yet annotated SC clusters to functional cell groups. HighlightsO_LIFirst single-cell transcriptomic atlas for Daphnia magna, identifies > 30 distinct cell types. C_LIO_LINovel cell type representing circulating hemocytes may play a role in cuticle regeneration. C_LIO_LIEvidence for non-circulating hemocyte-like cells associated with the fat body in Daphnia. C_LIO_LICuticle/epithelial cells expressing photoreceptors, suggesting light-sensing capabilities. C_LIO_LISubfunctionalization of divergent paralogs across cell types for ecological versatility. C_LI
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Transcriptional Profiling of Identified Neurons in Leech 95%
- Transcriptome analysis provides genome annotation and expression profiles in the central nervous system of Lymnaea stagnalis at different ages 94%
- The genome of New Zealand trevally (Carangidae: Pseudocaranx georgianus) uncovers a XY sex determination locus 94%
Similar papers in this journal
Similar papers in this journal
- The transcriptomics of phenotypic nonspecificity in Drosophila melanogaster 95%
- Loss of altruism in the social amoeba Dictyostelium discoideum is associated with the G protein-coupled receptor grlG 94%
- The chromosome-scale genome assembly of the yellowtail clownfish Amphiprion clarkii provides insights into melanic pigmentation of anemonefish 94%
Similar papers in this journal
- Temporal dynamics of gene expression during metamorphosis in two distant Drosophila species 96%
- Sexual antagonism and sex determination in three syngnathid species alongside male pregnancy gradient and varying sex roles. 95%
- Assessing the impact of whole genome duplication on gene expression and regulation during arachnid development 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.