Axon guidance requires interactions between extracellular signaling molecules and transmembrane receptors, but how appropriate context-dependent decisions are coordinated outside the cell remains unclear. Here we show that the transmembrane glycoprotein Dystroglycan interacts with a changing set of environmental cues that regulate the trajectories of extending axons throughout the mammalian brain and spinal cord. Dystroglycan operates primarily as an extracellular scaffold during axon guidance, as it functions non-cell autonomously and does not require signaling through its intracellular domain. We identify the transmembrane receptor Celsr3/Adgrc3 as a binding partner for Dystroglycan, and show that this interaction is critical for specific axon guidance events . These findings establish Dystroglycan as a multifunctional scaffold that coordinates extracellular matrix proteins, secreted cues, and transmembrane receptors to regulate axon guidance.

Determining the spatial organization and morphological characteristics of molecularly defined cell types is a major bottleneck for characterizing the architecture underpinning brain function. We developed Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to survey gene expression in brain tissue, as well as a turnkey computational pipeline to rapidly process large EASI-FISH image datasets. EASI-FISH was optimized for thick brain sections (300 μm) to facilitate reconstruction of spatio-molecular domains that generalize across brains. Using the EASI-FISH pipeline, we investigated the spatial distribution of dozens of molecularly defined cell types in the lateral hypothalamic area (LHA), a brain region with poorly defined anatomical organization. Mapping cell types in the LHA revealed nine spatially and molecularly defined subregions. EASI-FISH also facilitates iterative reanalysis of scRNA-seq datasets to determine marker-genes that further dissociated spatial and morphological heterogeneity. The EASI-FISH pipeline democratizes mapping molecularly defined cell types, enabling discoveries about brain organization.

Extrachromosomal DNA (ecDNA) is prevalent in human cancers and mediates high expression of oncogenes through gene amplification and altered gene regulation. Gene induction typically involves cis-regulatory elements that contact and activate genes on the same chromosome. Here we show that ecDNA hubs-clusters of around 10-100 ecDNAs within the nucleus-enable intermolecular enhancer-gene interactions to promote oncogene overexpression. ecDNAs that encode multiple distinct oncogenes form hubs in diverse cancer cell types and primary tumours. Each ecDNA is more likely to transcribe the oncogene when spatially clustered with additional ecDNAs. ecDNA hubs are tethered by the bromodomain and extraterminal domain (BET) protein BRD4 in a MYC-amplified colorectal cancer cell line. The BET inhibitor JQ1 disperses ecDNA hubs and preferentially inhibits ecDNA-derived-oncogene transcription. The BRD4-bound PVT1 promoter is ectopically fused to MYC and duplicated in ecDNA, receiving promiscuous enhancer input to drive potent expression of MYC. Furthermore, the PVT1 promoter on an exogenous episome suffices to mediate gene activation in trans by ecDNA hubs in a JQ1-sensitive manner. Systematic silencing of ecDNA enhancers by CRISPR interference reveals intermolecular enhancer-gene activation among multiple oncogene loci that are amplified on distinct ecDNAs. Thus, protein-tethered ecDNA hubs enable intermolecular transcriptional regulation and may serve as units of oncogene function and cooperative evolution and as potential targets for cancer therapy.

In Neuroscience, the structure of a circuit has often been used to intuit function - an inversion of Louis Kahn's famous dictum, `Form follows function' (Kristan and Katz 2006). However, different brain networks may utilize different network architectures to solve the same problem. The olfactory circuits of two insects, the Locust, and the fruit fly, , serve the same function - to identify and discriminate odors. The neural circuitry that achieves this shows marked structural differences. Projection neurons (PN) in the antennal lobe (AL) innervate Kenyon cells (KC) of the mushroom body (MB). In locust, each KC receives inputs from ∼50% PNs, a scheme that maximizes the difference between inputs to any two of ∼50,000 KCs. In contrast, in drosophila, this number is only 5% and appears sub-optimal. Using a computational model of the olfactory system, we show the activity of KCs is sufficiently high-dimensional that it can separate similar odors regardless of the divergence of PN-KC connections. However, when temporal patterning encodes odor attributes, dense connectivity outperforms sparse connections.Increased separability comes at the cost of reliability. The disadvantage of sparse connectivity can be mitigated by incorporating other aspects of circuit architecture seen in drosophila. Our simulations predict that drosophila and locust circuits lie at different ends of a continuum where the drosophila gives up on the ability to resolve similar odors to generalize across varying environments, while the locust separates odor representations but risks misclassifying noisy variants of the same odor. How does the structure of a network affect its function? We address this question in the context of two olfactory systems that serve the same function, to distinguish the attributes of different odorants, but do so using markedly distinct architectures. In the locust, the probability of connections between projection neurons and Kenyon cells - a layer downstream - is nearly 50%. In contrast, this number is merely 5% in drosophila. We developed computational models of these networks to understand the relative advantages of each connectivity. Our analysis reveals that the two systems exist along a continuum of possibilities that balance two conflicting goals - separating the representations of similar odors while grouping together noisy variants of the same odor.

With the development of neural recording technology, it has become possible to collect activities from hundreds or even thousands of neurons simultaneously. Visualization of neural population dynamics can help neuroscientists analyze large-scale neural activities efficiently. In this letter, Laplacian eigenmaps is applied to this task for the first time, and the experimental results show that the proposed method significantly outperforms the commonly used methods. This finding was confirmed by the systematic evaluation using nonhuman primate data, which contained the complex dynamics well suited for testing. According to our results, Laplacian eigenmaps is better than the other methods in various ways and can clearly visualize interesting biological phenomena related to neural dynamics.