Applying modern machine-vision techniques to the study of animal behavior, two groups developed systems that quantify many aspects of the complex social behaviors of Drosophila melanogaster. These software tools will enable high-throughput screens that seek to uncover the cellular and molecular underpinnings of behavior.

I am honored and humbled to receive the E. B. Wilson Medal and happy to share some reflections on my journey as a cell biologist. It took me a while to realize that my interest in biology would center on how cells are spatially and dynamically organized. From an initial fascination with cellular structures I came to appreciate that cells exhibit dynamism across all scales-from their molecules, to molecular complexes, to organelles. Uncovering the principles of this dynamism, including new ways to observe and quantify it, has been the guiding star of my work.

Oncogene amplification on extrachromosomal DNA (ecDNA) is a common event, driving aggressive tumor growth, drug resistance and shorter survival. Currently, the impact of nonchromosomal oncogene inheritance-random identity by descent-is poorly understood. Also unclear is the impact of ecDNA on somatic variation and selection. Here integrating theoretical models of random segregation, unbiased image analysis, CRISPR-based ecDNA tagging with live-cell imaging and CRISPR-C, we demonstrate that random ecDNA inheritance results in extensive intratumoral ecDNA copy number heterogeneity and rapid adaptation to metabolic stress and targeted treatment. Observed ecDNAs benefit host cell survival or growth and can change within a single cell cycle. ecDNA inheritance can predict, a priori, some of the aggressive features of ecDNA-containing cancers. These properties are facilitated by the ability of ecDNA to rapidly adapt genomes in a way that is not possible through chromosomal oncogene amplification. These results show how the nonchromosomal random inheritance pattern of ecDNA contributes to poor outcomes for patients with cancer.

The extracellular matrix (ECM) regulates cell migration and sculpts organ shape. AdamTS proteins are extracellular metalloproteases known to modify ECM proteins and promote cell migration, but demonstrated roles for AdamTS proteins in regulating CNS structure and ensuring cell lineages remain fixed in place have not been uncovered. Using forward genetic approaches in Drosophila, we find that reduction of AdamTS-A function induces both the mass exodus of neural lineages out of the CNS and drastic perturbations to CNS structure. Expressed and active in surface glia, AdamTS-A acts in parallel to perlecan and in opposition to viking/collagen IV and βPS-integrin to keep CNS lineages rooted in place and to preserve the structural integrity of the CNS. viking/collagen IV and βPS-integrin are known to promote tissue stiffness and oppose the function of perlecan, which reduces tissue stiffness. Our work supports a model in which AdamTS-A anchors cells in place and preserves CNS architecture by reducing tissue stiffness.

The structure of compound eyes in arthropods has been the subject of many studies, revealing important biological principles. Until recently, these studies were constrained by the two-dimensional nature of available ultrastructural data. By taking advantage of the novel three-dimensional ultrastructural dataset obtained using volume electron microscopy, we present the first cellular-level reconstruction of the whole compound eye of an insect, the miniaturized parasitoid wasp Megaphragma viggianii. The compound eye of the female M. viggianii consists of 29 ommatidia and contains 478 cells. Despite the almost anucleate brain, all cells of the compound eye contain nuclei. As in larger insects, the dorsal rim area of the eye in M. viggianii contains ommatidia that are believed to be specialized in polarized light detection as reflected in their corneal and retinal morphology. We report the presence of three 'ectopic' photoreceptors. Our results offer new insights into the miniaturization of compound eyes and scaling of sensory organs in general.

Preprint:  https://doi.org 10.1101/2024.09.30.615804

The brain's synaptic networks endow an animal with powerfully adaptive biological behavior. Maps of such synaptic circuits densely reconstructed in those model brains, which can be examined and manipulated by genetic means, offer the best prospect for understanding the underlying biological bases of behavior. That prospect is now technologically feasible and a scientifically enabling possibility in neurobiology, much as genomics has been in molecular biology and genetics. In , two major advances are in electron microscopic technology, using focused ion beam-scanning electron microscopy (FIB-SEM) milling to capture and align digital images, and in computer-aided reconstruction of neuron morphologies. The last decade has witnessed enormous progress in detailed knowledge of the actual synaptic circuits formed by real neurons. Advances in various brain regions that heralded identification of the motion-sensing circuits in the optic lobe are now extending to other brain regions, with the prospect of encompassing the fly's entire nervous system, both brain and ventral nerve cord. Expected final online publication date for the Volume 35 is October 7, 2019. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

In the fruit fly, Drosophila melanogaster, connectome data and genetic tools provide a unique opportunity to study complex behaviors including navigation, mating, aggression, and grooming in an organism with a tractable nervous system of 140,000 neurons. Here we present the Fly Disco, a flexible system for high quality video collection, optogenetic manipulation, and fine-grained behavioral analysis of freely walking and socializing fruit fly groups. The data collection hardware and software automates the collection of videos synced to programmable optogenetic stimuli. Key pipeline features include behavioral analysis based on trajectories of 21 keypoints and optogenetic-specific summary statistics and data visualization. We created the multifly dataset for pose estimation that includes 9701 examples enriched in complex behaviors. All hardware designs, software, and the multifly dataset are freely available.

Any method for RNA secondary structure prediction is determined by four ingredients. The architecture is the choice of features implemented by the model (such as stacked basepairs, loop length distributions, etc.). The architecture determines the number of parameters in the model. The scoring scheme is the nature of those parameters (whether thermodynamic, probabilistic, or weights). The parameterization stands for the specific values assigned to the parameters. These three ingredients are referred to as "the model." The fourth ingredient is the folding algorithms used to predict plausible secondary structures given the model and the sequence of a structural RNA. Here, I make several unifying observations drawn from looking at more than 40 years of methods for RNA secondary structure prediction in the light of this classification. As a final observation, there seems to be a performance ceiling that affects all methods with complex architectures, a ceiling that impacts all scoring schemes with remarkable similarity. This suggests that modeling RNA secondary structure by using intrinsic sequence-based plausible "foldability" will require the incorporation of other forms of information in order to constrain the folding space and to improve prediction accuracy. This could give an advantage to probabilistic scoring systems since a probabilistic framework is a natural platform to incorporate different sources of information into one single inference problem.

The primary auditory cortex (A1) is organized tonotopically, with neurons sensitive to high and low frequencies arranged in a rostro-caudal gradient. We used laser scanning photostimulation in acute slices to study the organization of local excitatory connections onto layers 2 and 3 (L2/3) of the mouse A1. Consistent with the organization of other cortical regions, synaptic inputs along the isofrequency axis (orthogonal to the tonotopic axis) arose predominantly within a column. By contrast, we found that local connections along the tonotopic axis differed from those along the isofrequency axis: some input pathways to L3 (but not L2) arose predominantly out-of-column. In vivo cell-attached recordings revealed differences between the sound-responsiveness of neurons in L2 and L3. Our results are consistent with the hypothesis that auditory cortical microcircuitry is specialized to the one-dimensional representation of frequency in the auditory cortex.