Coleoid cephalopods (octopus, squid, and cuttlefish) are active, resourceful predators with a rich behavioral repertoire1. They have the largest nervous systems among the invertebrates2 and present other striking morphological innovations including camera-like eyes, prehensile arms, a highly derived early embryogenesis, and the most sophisticated adaptive coloration system among all animals1,3. To investigate the molecular bases of cephalopod brain and body innovations we sequenced the genome and multiple transcriptomes of the California two-spot octopus, Octopus bimaculoides. We found no evidence for hypothesized whole genome duplications in the octopus lineage4–6. The core developmental and neuronal gene repertoire of the octopus is broadly similar to that found across invertebrate bilaterians, except for massive expansions in two gene families formerly thought to be uniquely enlarged in vertebrates: the protocadherins, which regulate neuronal development, and the C2H2 superfamily of zinc finger transcription factors. Extensive mRNA editing generates transcript and protein diversity in genes involved in neural excitability, as previously described7, as well as in genes participating in a broad range of other cellular functions. We identified hundreds of cephalopod-specific genes, many of which showed elevated expression levels in such specialized structures as the skin, the suckers, and the nervous system. Finally, we found evidence for large-scale genomic rearrangements that are closely associated with transposable element expansions. Our analysis suggests that substantial expansion of a handful of gene families, along with extensive remodeling of genome linkage and repetitive content, played a critical role in the evolution of cephalopod morphological innovations, including their large and complex nervous systems.

The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation in tools to quantify natural animal behavior. While advances in deep learning and computer vision have enabled markerless pose estimation in individual animals, extending these to multiple animals presents unique challenges for studies of social behaviors or animals in their natural environments. Here we present Social LEAP Estimates Animal Poses (SLEAP), a machine learning system for multi-animal pose tracking. This system enables versatile workflows for data labeling, model training and inference on previously unseen data. SLEAP features an accessible graphical user interface, a standardized data model, a reproducible configuration system, over 30 model architectures, two approaches to part grouping and two approaches to identity tracking. We applied SLEAP to seven datasets across flies, bees, mice and gerbils to systematically evaluate each approach and architecture, and we compare it with other existing approaches. SLEAP achieves greater accuracy and speeds of more than 800 frames per second, with latencies of less than 3.5 ms at full 1,024 × 1,024 image resolution. This makes SLEAP usable for real-time applications, which we demonstrate by controlling the behavior of one animal on the basis of the tracking and detection of social interactions with another animal.

The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation to quantify and model natural animal behavior. This has led to important advances in deep learning-based markerless pose estimation that have been enabled in part by the success of deep learning for computer vision applications. Here we present SLEAP (Social LEAP Estimates Animal Poses), a framework for multi-animal pose tracking via deep learning. This system is capable of simultaneously tracking any number of animals during social interactions and across a variety of experimental conditions. SLEAP implements several complementary approaches for dealing with the problems inherent in moving from single-to multi-animal pose tracking, including configurable neural network architectures, inference techniques, and tracking algorithms, enabling easy specialization and tuning for particular experimental conditions or performance requirements. We report results on multiple datasets of socially interacting animals (flies, bees, and mice) and describe how dataset-specific properties can be leveraged to determine the best configuration of SLEAP models. Using a high accuracy model (<2.8 px error on 95% of points), we were able to track two animals from full size 1024 × 1024 pixel frames at up to 320 FPS. The SLEAP framework comes with a sophisticated graphical user interface, multi-platform support, Colab-based GPU-free training and inference, and complete tutorials available, in addition to the datasets, at sleap.ai.

Cephalopods are known for their large nervous systems, complex behaviors and morphological innovations. To investigate the genomic underpinnings of these features, we assembled the chromosomes of the Boston market squid, Doryteuthis (Loligo) pealeii, and the California two-spot octopus, Octopus bimaculoides, and compared them with those of the Hawaiian bobtail squid, Euprymna scolopes. The genomes of the soft-bodied (coleoid) cephalopods are highly rearranged relative to other extant molluscs, indicating an intense, early burst of genome restructuring. The coleoid genomes feature multi-megabase, tandem arrays of genes associated with brain development and cephalopod-specific innovations. We find that a known coleoid hallmark, extensive A-to-I mRNA editing, displays two fundamentally distinct patterns: one exclusive to the nervous system and concentrated in genic sequences, the other widespread and directed toward repetitive elements. We conclude that coleoid novelty is mediated in part by substantial genome reorganization, gene family expansion, and tissue-dependent mRNA editing.

Post-reproductive life in the female octopus is characterized by an extreme pattern of maternal care: the mother cares for her clutch of eggs without feeding until her death. These maternal behaviors are eradicated if the optic glands, the octopus analog of the vertebrate pituitary gland, are removed from brooding females. Despite the optic gland's importance in regulating maternal behavior, the molecular features underlying optic gland function are unknown. Here, we identify major signaling systems of the optic gland. Through behavioral analyses and transcriptome sequencing, we report that the optic gland undergoes remarkable molecular changes that coincide with transitions between behavioral stages. These include the dramatic upregulation and downregulation of catecholamine, steroid, insulin and feeding peptide pathways. Transcriptome analyses in other tissues demonstrate that these molecular changes are not generalized markers of senescence, but instead, specific features of the optic glands. Our study expands the classic optic gland-pituitary gland analogy and more specifically, it indicates that, rather than a single 'self-destruct' hormone, the maternal optic glands employ multiple pathways as systemic hormonal signals of behavioral regulation.