@article {69722, title = {Days-old zebrafish rapidly learn to recognize threatening agents through noradrenergic and forebrain circuits.}, journal = {Curr Biol}, volume = {35}, year = {2025}, month = {12/2024}, pages = {163-176.e4}, abstract = {

Animals need to rapidly learn to recognize and avoid predators. This ability may be especially important for young animals due to their increased vulnerability. It is unknown whether, and how, nascent vertebrates are capable of such rapid learning. Here, we used a robotic predator-prey interaction assay to show that 1\ week after fertilization-a developmental stage where they have approximately 1\% the number of neurons of adults-zebrafish larvae rapidly and robustly learn to recognize a stationary object as a threat after the object pursues the fish for \~{}1\ min. Larvae continue to avoid the threatening object after it stops moving and can learn to distinguish threatening from non-threatening objects of a different color. Whole-brain functional imaging revealed the multi-timescale activity of noradrenergic neurons and forebrain circuits that encoded the threat. Chemogenetic ablation of those populations prevented the learning. Thus, a noradrenergic and forebrain multiregional network underlies the ability of young vertebrates to rapidly learn to recognize potential predators within their first week of life.

}, keywords = {Adrenergic Neurons, Animals, Larva, Learning, Predatory Behavior, Prosencephalon, Zebrafish}, issn = {1879-0445}, doi = {10.1016/j.cub.2024.11.057}, author = {Zocchi, Dhruv and Nguyen, Millen and Marquez-Legorreta, Emmanuel and Siwanowicz, Igor and Singh, Chanpreet and Prober, David A and Hillman, Elizabeth M C and Ahrens, Misha B} } @article {70224, title = {SpotDMix: informed mRNA transcript assignment using mixture models}, journal = {bioRxiv}, year = {2025}, month = {2025 Dec 18}, abstract = {

Unveiling the genetic profiles of spatially distinguished cells is an important aspect in many areas of brain research, as the genetic identity contains information about a cell{\textquoteright}s physiological properties and internal state. On top of this, knowledge of the genetic details of each cell can reveal structural organization within tissue. As image-based spatial transcriptomics moves toward applications in tissues with dense cellular packing, accurate assignment of detected mRNA transcripts ("spots") to correct segmented cells becomes increasingly difficult, rendering simple methods insufficient with many incorrect assignments to neighboring cells. Here we introduce SpotDMix, a statistical model for assigning spots to cells by modeling spots as coming from a mixture model of distributions matching segmented cell shapes, with assignment probabilities and shape parameters optimized using the Expectation Maximization algorithm. Performance is assessed and compared against several simple methods in various scenarios on both surrogate data and larval zebrafish data. In all tested scenarios SpotDMix outperforms the simple methods on all evaluated metrics, including individual transcript assignment accuracy, total assigned number of spots per cell error and cell type classification. Further, SpotDMix produces a higher degree of exclusivity between genes which are known to not or rarely co-express.

}, doi = {10.64898/2025.12.15.693918}, url = {https://www.biorxiv.org/content/early/2025/12/18/2025.12.15.693918}, author = {Smeets, Kasper and Hesselink, Luuk W and Marquez-Legorreta, Emmanuel and Fleishman, Greg M and Eddison, Mark and Tillberg, Paul W and Ahrens, Misha B and Englitz, Bernhard} } @article {69706, title = {Days-old zebrafish rapidly learn to recognize threatening agents through noradrenergic and forebrain circuits.}, journal = {Curr Biol}, year = {2024}, month = {2024 Dec 19}, abstract = {

Animals need to rapidly learn to recognize and avoid predators. This ability may be especially important for young animals due to their increased vulnerability. It is unknown whether, and how, nascent vertebrates are capable of such rapid learning. Here, we used a robotic predator-prey interaction assay to show that 1\ week after fertilization-a developmental stage where they have approximately 1\% the number of neurons of adults-zebrafish larvae rapidly and robustly learn to recognize a stationary object as a threat after the object pursues the fish for \~{}1\ min. Larvae continue to avoid the threatening object after it stops moving and can learn to distinguish threatening from non-threatening objects of a different color. Whole-brain functional imaging revealed the multi-timescale activity of noradrenergic neurons and forebrain circuits that encoded the threat. Chemogenetic ablation of those populations prevented the learning. Thus, a noradrenergic and forebrain multiregional network underlies the ability of young vertebrates to rapidly learn to recognize potential predators within their first week of life.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2024.11.057}, author = {Zocchi, Dhruv and Nguyen, Millen and Marquez-Legorreta, Emmanuel and Siwanowicz, Igor and Singh, Chanpreet and Prober, David A and Hillman, Elizabeth M C and Ahrens, Misha B} }