Updated single cell reference atlas for the starlet anemone Nematostella vectensis.

BACKGROUND: The recent combination of genomics and single cell transcriptomics has allowed to assess a variety of non-conventional model organisms in much more depth. Single cell transcriptomes can uncover hidden cellular complexity and cell lineage relationships within organisms. The recent developmental cell atlases of the sea anemone Nematostella vectensis, a representative of the basally branching Cnidaria, has provided new insights into the development of all cell types (Steger et al Cell Rep 40(12):111370, 2022; Sebé-Pedrós et al. Cell 173(6):1520-1534.e20). However, the mapping of the single cell reads still suffers from relatively poor gene annotations and a draft genome consisting of many scaffolds. RESULTS: Here we present a new wildtype resource of the developmental single cell atlas, by re-mapping of sequence data first published in Steger et al. (2022) and Cole et al. (Nat Commun 14(1):1747, 2023), to the new chromosome-level genome assembly and corresponding gene models in Zimmermann et al. (Nat Commun 14, 8270 (2023). https://doi.org/10.1038/s41467-023-44080-7 ). We expand the pre-existing dataset through the incorporation of additional sequence data derived from the capture and sequencing of cell suspensions from four additional samples: 24 h gastrula, 2d planula, an inter-parietal region of the bodywall from a young unsexed animal, and another adult mesentery from a mature male animal. CONCLUSION: Our analyses of the full cell-state inventory provide transcriptomic signatures for 127 distinct cell states, of which 47 correspond to neuroglandular subtypes. We also identify two distinct putatively immune-related transcriptomic profiles that segregate between the inner and outer cell layers. Furthermore, the new gene annotation Nv2 has markedly improved the mapping on the single cell transcriptome data and will therefore be of great value for the community and anyone using the dataset.

EmbryoNet: using deep learning to link embryonic phenotypes to signaling pathways.

Evolutionarily conserved signaling pathways are essential for early embryogenesis, and reducing or abolishing their activity leads to characteristic developmental defects. Classification of phenotypic defects can identify the underlying signaling mechanisms, but this requires expert knowledge and the classification schemes have not been standardized. Here we use a machine learning approach for automated phenotyping to train a deep convolutional neural network, EmbryoNet, to accurately identify zebrafish signaling mutants in an unbiased manner. Combined with a model of time-dependent developmental trajectories, this approach identifies and classifies with high precision phenotypic defects caused by loss of function of the seven major signaling pathways relevant for vertebrate development. Our classification algorithms have wide applications in developmental biology and robustly identify signaling defects in evolutionarily distant species. Furthermore, using automated phenotyping in high-throughput drug screens, we show that EmbryoNet can resolve the mechanism of action of pharmaceutical substances. As part of this work, we freely provide more than 2 million images that were used to train and test EmbryoNet.

Muscle cell-type diversification is driven by bHLH transcription factor expansion and extensive effector gene duplications.

Animals are typically composed of hundreds of different cell types, yet mechanisms underlying the emergence of new cell types remain unclear. Here we address the origin and diversification of muscle cells in the non-bilaterian, diploblastic sea anemone Nematostella vectensis. We discern two fast and two slow-contracting muscle cell populations, which differ by extensive sets of paralogous structural protein genes. We find that the regulatory gene set of the slow cnidarian muscles is remarkably similar to the bilaterian cardiac muscle, while the two fast muscles differ substantially from each other in terms of transcription factor profiles, though driving the same set of structural protein genes and having similar physiological characteristics. We show that anthozoan-specific paralogs of Paraxis/Twist/Hand-related bHLH transcription factors are involved in the formation of fast and slow muscles. Our data suggest that the subsequent recruitment of an entire effector gene set from the inner cell layer into the neural ectoderm contributes to the evolution of a novel muscle cell type. Thus, we conclude that extensive transcription factor gene duplications and co-option of effector modules act as an evolutionary mechanism underlying cell type diversification during metazoan evolution.