The structure and interaction networks of molecules within biomolecular condensates are poorly understood. Using cryo-electron tomography and molecular dynamics simulations, we elucidated the structure of phase-separated chromatin condensates across scales, from individual amino acids to network architecture. We found that internucleosomal DNA linker length controls nucleosome arrangement and histone tail interactions, shaping the structure of individual chromatin molecules within and outside condensates. This structural modulation determines the balance between intra- and intermolecular interactions, which governs the molecular network, thermodynamic stability, and material properties of chromatin condensates. Mammalian nuclei contain dense clusters of nucleosomes whose nonrandom organization is mirrored by the reconstituted condensates. Our work explains how the structure of individual chromatin molecules determines physical properties of chromatin condensates and cellular chromatin organization.

Mainstream medicine commonly categorizes acupuncture as “alternative and complementary,” a designation that reflects conceptual gaps in existing treatment classification systems. Integrating complementary medicine into the mainstream medical system requires a conceptual adjustment. Here, I propose a mechanism-based 5R classification—Removing, Repairing, Replacing, Replenishing, Regulating—to systematically categorize therapies. Based on this classification, acupuncture and its related interventions fall under functional regulation therapy. This framework offers a unified, functional perspective that facilitates the integration of complementary medicine into mainstream medical taxonomy.

Maintaining physiological homeostasis requires a complex interplay among endocrine organs, peripheral tissues, and distributed neuroendocrine control circuits, all of which are coupled through feedback loops that operate over minutes to hours. Although many physiological needs are broadcast through hormones, metabolites, and other chemical compounds circulating in the bloodstream, we rarely observe more than a few of these messengers together and at high cadence during behavior. To address this, we developed a minimally disruptive workflow to measure the free fraction of hundreds of amines and small peptides at a 7.5-minute cadence for \~8 hrs in freely moving mice using chronic jugular microdialysis implants and chemical isotope labeling Liquid Chromatography-Mass Spectrometry. Single-compound profiles behave according to known physiology, such as purine turnover correlating with movement, delayed histamine/5-HIAA changes, and coordinated amino-acid dynamics. Our multiplexed measures enable high-dimensional analyses that uncover properties of the underlying dynamics. For example, systems-level analyses show that 10 dimensions explain over 70% of the variance in hormone/metabolite covariation, consistent with a low rank description of the physiological state space, with projections aligned to locomotion state transitions. Our work opens avenues for the discovery of hormonal dynamics, compound interactions, and their effects on behavior.

During brief, intermittent “replay” events, hippocampal activity can express navigational trajectories disconnected from both when and where they originally occurred. While replay biased toward immediate future goals has been observed, there is no evidence yet linking replay to planning beyond the next action. Here, we designed a sequential spatial working memory task which required rats to utilize information across multiple temporally separated actions. Remote replay events matched the animal’s future navigational choices made after completing an intervening subtask. Critically, this occurred only when the replayed information was useful for reducing memory load, consistent with it being an active process. Our findings suggest these remote replay events are a neural correlate of episodic forethought, allowing animals to use memories to plan beyond their immediate surroundings.

No abstract available.

Background/Objectives: High-grade gliomas (HGGs), including glioblastomas, are among the most aggressive brain tumorså due to their high intratumoral heterogeneity and extensive infiltration. Glioma stem-like cells (GSCs) frequently invade along white matter tracts such as the corpus callosum, but the molecular programs driving this region-specific invasion remain poorly defined. The aim of this study was to identify transcriptional signatures associated with GSC infiltration into the corpus callosum. Methods: We established an orthotopic xenograft model by implanting fluorescently labeled human GSCs into nude mouse brains. Tumor growth and invasion patterns were assessed using tissue clearing, light-sheet fluorescence microscopy, and histological analyses. To characterize region-specific molecular profiles, we performed microfluidic-based single-cell RNA expression analysis of 48 invasion- and stemness-related genes in cells isolated from the tumor bulk (TB) and corpus callosum (CC). Results: By six weeks post-implantation, GSCs displayed marked tropism for the corpus callosum, with distinct infiltration patterns captured by three-dimensional imaging. Single-cell gene expression profiling revealed significant differences in 7 of the 48 genes (14.6%) between TB- and CC-derived GSCs. These genes—NES, CCND1, GUSB, NOTCH1, E2F1, EGFR, and TGFB1—collectively defined a “corpus callosum invasion signature” (CC-Iv). CC-derived cells showed a unimodal, high-expression profile of CC-Iv genes, whereas TB cells exhibited bimodal distributions, suggesting heterogeneous transcriptional states. Importantly, higher CC-Iv expression correlated with worse survival in patients with low-grade gliomas. Conclusions: This multimodal approach identified a corpus callosum-specific invasion signature in glioma stem-like cells, revealing how local microenvironmental cues shape transcriptional reprogramming during infiltration. These findings provide new insights into the spatial heterogeneity of gliomas and highlight potential molecular targets for therapies designed to limit tumor spread through white matter tracts.

To understand the neural basis of behavior, it is essential to sensitively and accurately measure neural activity at single-neuron and single-spike resolution. Extracellular electrophysiology delivers this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To minimize these limitations, we developed a silicon probe with much smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). Using NP Ultra, neuronal yield in mouse visual cortex recordings increased by more than 2-fold. With ultra-high spatial resolution, we discovered that a feature of extracellular waveforms, the spatial extent or "footprint," distinguished axonal from somatic recordings. In addition, three genetically identified cortical cell types could be discriminated from one another with ∼80% accuracy and from other neurons with ∼85% accuracy. NP Ultra improves yield, detection of subcellular compartments, and cell type identification to enable a more powerful dissection of neural circuit activity during behavior.

bioRxiv preprint: doi.org/10.1101/2023.08.23.554527

The first step to probing any potential interaction between two biomolecules is to determine their spatial association. In other words, if two biomolecules localize similarly within a cell, then it is plausible they could interact. Traditionally, this is quantified through various colocalization metrics. These measures infer this association by estimating the degree to which fluorescent signals from each biomolecule overlap or correlate. However, these metrics are, at best, proxies, and they depend strongly on various experimental choices. Alternatively, here we define a new strategy which leverages multispectral imaging and phasor analysis, termed the Phasor Mixing Coefficient (PMC). PMC measures the precise mixing of fluorescent signals in each pixel. We demonstrate how PMC captures complex biological subtlety by offering two distinct values, a global measure of overall color mixing and the homogeneity thereof. We additionally show that PMC exhibits less sensitivity to signal-to-noise ratio, intensity threshold, and background signal compared to canonical methods. Moreover, this method provides a means to visualize color mixing at each pixel. We show that PMC offers users a nuanced and robust metric to quantify biological association.

The ability to use generalized prior experience to guide behavior in novel situations is a fundamental cognitive function. While recent evidence suggests that the hippocampus supports generalization how this is accomplished is poorly understood. Here we combined longitudinal optical imaging in head-fixed mice with computational modeling to examine generalization in hippocampal area CA1. We found that prior training accelerated behavioral adaptation to a novel environment and that this was accompanied by highly stable hippocampal representations. We identified putative memory traces from prior experience that enabled this generalization at multiple levels. At the population level, novel-context network dynamics rapidly aligned with low-dimensional neural subspaces established during prior experience. At the cellular level, spatially-informative weak "residual" activity reflecting generalizable information about the task structure appeared to bias which neurons form place fields (PFs) and where via behavioral timescale synaptic plasticity (BTSP). Finally, this was an active process as many PFs changed their reference frame in the novel environment to reflect the consistent task structure. In sum, the influence of memory traces on new PF formation may allow past experience to guide new learning such that representations are based on generalizable features, thus enabling rapid adaptive behavior in new contexts.

The first step to probing any potential interaction between two biomolecules is to determine their spatial association. In other words, if two biomolecules localize similarly within a cell, then it is plausible they could interact. Traditionally, this is quantified through various colocalization metrics. These measures infer this association by estimating the degree to which fluorescent signals from each biomolecule overlap or correlate. However, these metrics are, at best, proxies, and they depend strongly on various experimental choices. Alternatively, here we define a new strategy which leverages multispectral imaging and phasor analysis, termed the Phasor Mixing Coefficient (PMC). PMC measures the precise mixing of fluorescent signals in each pixel. We demonstrate how PMC captures complex biological subtlety by offering two distinct values, a global measure of overall color mixing and the homogeneity thereof. We additionally show that PMC exhibits less sensitivity to signal-to-noise ratio, intensity threshold, and background signal compared to canonical methods. Moreover, this method provides a means to visualize color mixing at each pixel. We show that PMC offers users a nuanced and robust metric to quantify biological association.