Background/Objectives: High-grade gliomas (HGGs), including glioblastomas, are among the most aggressive brain tumors 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.

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.

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.

The actin cytoskeleton is a fundamental and highly conserved structure that functions in diverse cellular processes, yet its direct contribution to organismal aging remains unclear. Here, we systematically interrogated how genetic and pharmacologic perturbations of actin structure and function influence lifespan and various hallmarks of aging in Caenorhabditis elegans. Whole-animal and tissue-specific knockdown of actin and key actin-binding proteins (ABPs) - arx-2 (Arp2/3), unc-60 (cofilin), and lev-11 (tropomyosin) - led to premature disruption of filament organization, reduced lifespan, and tissue-specific physiological defects. Bulk and single-nucleus RNA-sequencing revealed that ABP knockdowns elicited a strongly “aged” transcriptome. Actin dysfunction broadly exacerbated many age-associated phenotypes, including mitochondrial dysfunction, lipid dysregulation, loss of proteostasis, impaired autophagy, and intestinal barrier failure. Pharmacological destabilization with Latrunculin A mirrored genetic knockdowns, while mild stabilization with Jasplakinolide modestly extended lifespan, emphasizing that optimal and finely-tuned actin function is critical for healthy aging. Finally, analysis of human genome-wide association data revealed that common ACTB polymorphisms correlate with differences in age-related decline in gait speed, suggesting evolutionary conservation of actin’s role in healthy aging. Taken together, our results provide a comprehensive and publicly accessible resource that maps, for the first time, how actin integrity intersects with diverse aging pathways across tissues and scales. This descriptive framework is intended to enable future mechanistic discovery by offering a deep, unbiased dataset that can be integrated with emerging studies to define how actin dynamics contribute to aging.

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.

Most behaviors involve neural dynamics in high-dimensional activity spaces. A common approach is to extract dimensions that capture task-related variability, such as those separating stimuli or choices, yielding low-dimensional, task-aligned neural activity subspaces (“coding dimensions”). However, whether these dimensions actively drive decisions or merely reflect underlying computations remains unclear. Moreover, neural activity outside these coding subspaces (“residual dimensions”) is often ignored, though it could also causally shape neural dynamics driving behavior. We developed a recurrent neural network model that fits population activity and uncovers the dynamic interactions between coding and residual subspaces on single trials. Applied to electrophysiological recordings from the anterior lateral motor cortex (ALM) and motor thalamus in mice performing a delayed response task, our model demonstrates that perturbations of residual dimensions reliably alter behavioral choices, whereas perturbations of the choice dimension, which strongly encodes the animal’s upcoming decision, are largely ineffective. These perturbation effects arise because residual dimensions drive transient amplification across an intermediate number of coding and residual dimensions (\~10), before the dynamics collapse into discrete attractor states corresponding to the animal’s choice. By dissecting the low-dimensional variability underlying error trials, we find that it primarily shifts trajectories along residual dimensions, biasing single decisions. Residual activity in thalamus shapes cortical decision dynamics, implicating weakly selective thalamic populations in the emergence of cortical selectivity. Our findings challenge the conventional focus on low-dimensional coding subspaces as sufficient framework for understanding neural computations, demonstrating that dimensions previously considered task-irrelevant and accounting for little variance can have a critical role in driving behavior.

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.

Geroscience aims to target the aging process to extend healthspan. However, even isogenic individuals show heterogeneity in natural aging rate and responsiveness to pro-longevity interventions, limiting translational potential. Using RNAseq analysis of young, isogenic, subpopulations of Caenorhabditis elegans selected solely on the basis of the splicing pattern of an in vivo minigene reporter that is predictive of future life expectancy, we find a strong correlation in young animals between predicted life span and alternative splicing of mRNAs related to lipid metabolism. The activity of two RNA splicing factors, Reversed Polarity-1 (REPO-1) and Splicing Factor 1 (SFA-1), early in life is necessary for C. elegans response to specific longevity interventions and leads to context-specific changes to fat content that is mirrored by knockdown of their direct target POD-2/ACC1. Moreover, POD-2/ACC1 is required for the same longevity interventions as REPO-1/SFA-1. In addition, early inhibition of REPO-1 renders animals refractory to late onset suppression of the TORC1 pathway. Together, we propose that splicing factor activity establishes a cellular landscape early in life that enables responsiveness to specific longevity interventions and may explain variance in efficacy between individuals.

Movement-related activity has been detected across much of the brain, including sensory and motor regions. However, much remains unknown regarding the distribution of movement-related activity across brain regions, and how this activity relates to neural computation. Here we analyzed movement-related activity in brain-wide recordings of more than 50,000 neurons in mice performing a decision-making task. We used multiple machine learning methods to predict neural activity from videography and found that movement-related signals differed across areas, with stronger movement signals close to the motor periphery and in motor-associated subregions. Delineating activity that predicts or follows movement revealed fine-scale structure of sensory and motor encoding across and within brain areas. Through single-trial video-based predictions of behavior, we identified activity modulation by uninstructed movements and their impact on choice-related activity analysis. Our work provides a map of movement encoding across the brain and approaches for linking neural activity, uninstructed movements and decision-making.