From visual perception to language, sensory stimuli change their meaning depending on previous experience. Recurrent neural dynamics can interpret stimuli based on externally cued context, but it is unknown whether they can compute and employ internal hypotheses to resolve ambiguities. Here we show that mouse retrosplenial cortex (RSC) can form several hypotheses over time and perform spatial reasoning through recurrent dynamics. In our task, mice navigated using ambiguous landmarks that are identified through their mutual spatial relationship, requiring sequential refinement of hypotheses. Neurons in RSC and in artificial neural networks encoded mixtures of hypotheses, location and sensory information, and were constrained by robust low-dimensional dynamics. RSC encoded hypotheses as locations in activity space with divergent trajectories for identical sensory inputs, enabling their correct interpretation. Our results indicate that interactions between internal hypotheses and external sensory data in recurrent circuits can provide a substrate for complex sequential cognitive reasoning.

Preprint: https://doi.org/10.1101/2022.04.12.488024v1

As animals adapt to new situations, neuromodulation is a potent way to alter behavior, yet mechanisms by which neuromodulatory nuclei compute during behavior are underexplored. The serotonergic raphe supports motor learning in larval zebrafish by visually detecting distance traveled during swims, encoding action effectiveness, and modulating motor vigor. We found that swimming opens a gate for visual input to cause spiking in serotonergic neurons, enabling encoding of action outcomes and filtering out learning-irrelevant visual signals. Using light-sheet microscopy, voltage sensors, and neurotransmitter/modulator sensors, we tracked millisecond-timescale neuronal input-output computations during behavior. Swim commands initially inhibited serotonergic neurons via GABA, closing the gate to spiking. Immediately after, the gate briefly opened: voltage increased consistent with post-inhibitory rebound, allowing swim-induced visual motion to evoke firing through glutamate, triggering serotonin secretion and modulating motor vigor. Ablating GABAergic neurons impaired raphe coding and motor learning. Thus, serotonergic neuromodulation arises from action-outcome coincidence detection within the raphe, suggesting the existence of similarly fast and precise circuit computations across neuromodulatory nuclei.

Preprint: https://doi.org/10.1101/2024.09.15.613083

Neurochemical signals like dopamine (DA) play a crucial role in a variety of brain functions through intricate interactions with other neuromodulators and intracellular signaling pathways. However, studying these complex networks has been hindered by the challenge of detecting multiple neurochemicals in vivo simultaneously. To overcome this limitation, we developed a single-protein chemigenetic DA sensor, HaloDA1.0, which combines a cpHaloTag-chemical dye approach with the G protein-coupled receptor activation-based (GRAB) strategy, providing high sensitivity for DA, sub-second response kinetics, and an extensive spectral range from far-red to near-infrared. When used together with existing green and red fluorescent neuromodulator sensors, Ca2+ indicators, cAMP sensors, and optogenetic tools, HaloDA1.0 provides high versatility for multiplex imaging in cultured neurons, brain slices, and behaving animals, facilitating in-depth studies of dynamic neurochemical networks.

AMPA-type receptors are transported large distances to support synaptic plasticity at distal dendritic locations. Studying the motion of AMPA receptor+ vesicles can improve our understanding of the mechanisms that underlie learning and memory. Nevertheless, technical challenges that prevent the visualization of AMPA receptor+vesicles limit our ability to study how these vesicles are trafficked. Existing methods rely on the overexpression of fluorescent protein-tagged AMPA receptors from plasmids, resulting in a saturated signal that obscures vesicles. Photobleaching must be applied to detect individual AMPA receptor+ vesicles, which may eliminate important vesicle populations from analysis. Here, we present a protocol to study AMPA receptor+ vesicles that addresses these challenges by 1) tagging AMPA receptors expressed from native loci with HaloTag and 2) employing a block-and-chase strategy with Janelia Fluor-conjugated HaloTag ligand to achieve sparse AMPA receptor labeling that obviates the need for photobleaching. After timelapse imaging is performed, AMPA receptor+ vesicles can be identified during image analysis, and their motion can be characterized using a single-particle tracking pipeline.

Key features

• Track and characterize the motion of AMPAR GluA1+ vesicles in cultured rat hippocampal neurons.

• GluA1 tagged with HaloTag (GluA1-HT) is expressed from native Gria1 loci to avoid overexpression.

• Sparse GluA1-HT labeling densities can be achieved without photobleaching via a block-and-chase strategy that utilizes Janelia Fluor (JF) dyes conjugated to HaloTag ligand (HTL).

• GluA1-HT+ vesicles are identified during image analysis, and their motion is characterized using single-particle tracking (SPT) and hidden Markov modeling with Bayesian model selection (HMM-Bayes).

Fluctuations and propagation of cytosolic calcium levels at both the cellular and tissue levels show complex patterns, referred to as calcium signatures, that regulate growth, organ development, damage responses, and survival. The quantitative analysis of calcium signatures at the cellular level is essential for identifying unique patterns that coordinate biological processes. However, a versatile framework applicable to multiple tissue types, allowing researchers to compare, measure, and validate diverse responses and recognize conserved patterns across model organisms, is missing. Here, we present a post-processing tool, CalciumInsights, which leverages the R packages Shiny and Golem. This tool has a graphical user interface and does not require software programming experience to perform calcium signal analysis. The open-source software has a modular framework with standardized functionalities that can be tailored for various research approaches. CalciumInsights provides descriptive statistical analysis through various metrics extracted from dynamic calcium transients and oscillations, such as peak amplitude, area under the curve, frequency, among others. The tool was evaluated with fluorescence imaging data from three model organisms: Danio rerio, Arabidopsis thaliana, and Drosophila melanogaster, demonstrating its ability to analyze diverse biological responses and models. Finally, the open-source nature of CalciumInsights enables community-driven improvements and developments for enabling new applications.

Author Summary: This manuscript introduces CalciumInsights, an open-source tool for calcium signature analysis. Designed to be a versatile tool that works with various tissue types and biological systems, CalciumInsights has an easy-to-use graphical user interface. Our program simplifies metrics extraction while maintaining the quality of the analysis by integrating several algorithms. CalciumInsights stands out for its user-friendliness, ease of use, and robust data exploration features, such as tunable filters for improved accuracy. These features promote inclusivity and lower barriers to scientific research by making calcium signature analysis accessible to users of all programming skill levels.

Dense-core vesicles (DCVs) are found in various types of cells, such as neurons, pancreatic β-cells, and chromaffin cells. These vesicles release transmitters, peptides, and hormones to regulate diverse functions, such as the stress response, immune response, behavior, and blood glucose levels. In traditional electron microscopy after chemical fixation, it is often reported that the dense cores occupy a portion of the vesicle towards the center and are surrounded by a clear halo. With electron microscopy following cryo-fixation in adrenal chromaffin cells, we report here that we did not observe halos, but dense cores filling up the entire vesicles suggesting that halos are likely the product of chemical fixation. More importantly, we observed that a fraction of DCVs contained 36-168 nm clear-core vesicles. A similar fraction of DCVs labeled with fluorescent false neurotransmitter FFN 511 or the dense-core matrix protein chromogranin A (CGA) were colocalized with fluorescently labeled or endogenous CD63 or ALIX, the membrane or lumen marker of ∼40-160 nm exosomes. These results suggest that DCVs contain exosomes. Since exosomes are generally thought to reside within multivesicular bodies in the cytosol and are released to the extracellular space to mediate diverse cell-to-cell communications, our findings suggest that dense-core vesicle fusion from many cell types is a new source for releasing exosomes to mediate intercellular communications. Given that dense-core vesicle fusion mediates many physiological functions, such as stress responses, immune responses, behavior regulation, and blood glucose regulation, exosome release from dense-core vesicle fusion might contribute to mediating these important functions.

Metabolism is fundamental to organism physiology and pathology. From the intricate network of metabolic reactions, diverse chemical molecules, collectively termed as metabolites, are produced. In multicellular organisms, metabolite communication between different tissues is vital for maintaining homeostasis and adaptation. However, the molecular mechanisms mediating these metabolite communications remain poorly understood. Here, we focus on nucleosides and nucleotides, essential metabolites involved in multiple cellular processes, and report the pivotal role of the SLC29A family of transporters in mediating nucleoside coordination between the soma and the germline. Through genetic analysis, we discovered that two Caenorhabditis elegans homologs of SLC29A transporters, Equilibrative Nucleoside Transporter ENT-1 and ENT-2, act in the germline and the intestine, respectively, to regulate reproduction. Their knockdown synergistically results in sterility. Further single-cell transcriptomic and targeted metabolomic profiling revealed that the ENT double knockdown specifically affects genes in the purine biosynthesis pathway and reduces the ratio of guanosine to adenosine levels. Importantly, guanosine supplementation into the body cavity/pseudocoelom through microinjection rescued the sterility caused by the ENT double knockdown, whereas adenosine microinjection had no effect. Together, these studies support guanosine as a rate limiting factor in the control of reproduction, uncover the previously unknown nucleoside/nucleotide communication between the soma and the germline essential for reproductive success, and highlight the significance of SLC-mediated cell-nonautonomous metabolite coordination in regulating organism physiology.

Preprint: https://doi.org/10.1101/2024.09.12.612591

Epigenome is sensitive to metabolic inputs and crucial for aging. Lysosomes emerge as a signaling hub to sense metabolic cues and regulate longevity. We unveil that lysosomal metabolic pathways signal through the epigenome to regulate transgenerational longevity in Caenorhabditis elegans. We discovered that the induction of lysosomal lipid signaling and lysosomal AMP-activated protein kinase (AMPK), or the reduction of lysosomal mechanistic-target-of-rapamycin (mTOR) signaling, increases the expression of histone H3.3 variant and elevates H3K79 methylation, leading to lifespan extension across multiple generations. This transgenerational pro-longevity effect requires intestine-to-germline transportation of H3.3 and a germline-specific H3K79 methyltransferase, and can be recapitulated by overexpressing H3.3 or the H3K79 methyltransferase. This work uncovers a lysosome-epigenome signaling axis linking soma and germline to mediate the transgenerational inheritance of longevity.Competing Interest StatementThe authors have declared no competing interest.National Institutes of Health, RF1AG074540, DP1DK113644Howard Hughes Medical Institute, https://ror.org/006w34k90

e13028Background: Liquid biopsy has emerged as a powerful, minimally invasive tool for predicting treatment response and survival in breast and other advanced cancers. However, the detection and characterization of circulating tumor cells (CTCs) — a key factor in metastatic progression—remain challenging due to their low frequency and reliance on manual, time-intensive validation using only a couple of established methods for immunofluorescence staining, such as CellSearch. Harnessing deep learning for automated CTC detection and characterization of the blood cells interacting with CTCs holds the potential to advance prognostic evaluations and guide more effective therapies significantly. Methods: Leveraging FDA-approved CellSearch technology and sequencing approaches, we analyzed 2,853 blood specimens, longitudinally collected from 1358 patients with advanced cancer (breast, prostate, etc) and additional diseases. We built a novel deep learning platform, CTCpose, which integrates machine learning and AI-driven image analysis to automate the detection and categorization of CTCs, white blood cells (WBCs), and their clustering interactions. We extracted cellular and nuclear features to enable precise evaluation of individual CTCs, WBCs, homotypic CTC clusters, heterotypic CTC–WBC clusters, and immune cell aggregates. Results: By employing the CTCpose platform, we achieved fully automated identification of CTCs and immune cells, unraveling the spatial organization and functional characteristics of both homotypic and heterotypic clusters. These highly granular assessments revealed clinically significant correlations with patient survival, disease progression, and therapeutic outcomes. Our data underscore the critical role of CTC–immune cell interactions and the dynamic shifts in CTC phenotypes—both as single cells and clusters—in stratifying patients by risk and informing treatment strategies. Conclusions: This work illustrates the transformative power of deep learning in the analysis of liquid biopsy samples. By overcoming the limitations of traditional CTC detection, we have established a robust framework that integrates imaging data with large-scale patient cohorts to deliver predictive models of high clinical relevance. The CTCpose platform not only refines our understanding of CTC–immune cell biology but also paves the way for personalized oncology approaches, highlighting the impactful convergence of artificial intelligence and precision medicine.