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

Using phase-diversity wavefront sensing, machine learning-based deformable mirror control, and neural representations of the unknown sample and phase aberration, we demonstrate precise reconstruction of sample structures in fluorescence microscopy, even under severe, high-order aberrations.Full-text article not available; see video presentation

We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images.

Single-walled carbon nanotubes (SWCNTs) functionalized with single-stranded DNAs can function as near-infrared nanosensors for molecular analytes. However, predicting which analytes elicit strong optical responses for specific nanosensors remains challenging. We developed machine learning (ML) models to predict analyte-induced fluorescence changes in a DNA–SWCNT dopamine nanosensor. Using a data set of 63 small molecules sampling chemical space around dopamine, we encoded analytes with RDKit fingerprints, with or without HOMO and LUMO energies, and applied principal component analysis to identify structural motifs associated with optical response strength. We trained support vector regression and classification models using two strategies: ensembles of 200 models and cross-validation. Regression models achieved mean R2 values of 0.2–0.4, with cross-validation outperforming ensembles, while classifiers reached mean F1 scores of ∼0.8. Cross-validation performed best for predictions on a blind set of 21 molecules. These findings show that ML can capture structure–response patterns in modest data sets and guide in silico DNA–SWCNT nanosensor design.

Insects constitute the most species-rich radiation of metazoa, a success that is due to the evolution of active flight. Unlike pterosaurs, birds and bats, the wings of insects did not evolve from legs, but are novel structures that are attached to the body via a biomechanically complex hinge that transforms tiny, high-frequency oscillations of specialized power muscles into the sweeping back-and-forth motion of the wings. The hinge consists of a system of tiny, hardened structures called sclerites that are interconnected to one another via flexible joints and regulated by the activity of specialized control muscles. Here we imaged the activity of these muscles in a fly using a genetically encoded calcium indicator, while simultaneously tracking the three-dimensional motion of the wings with high-speed cameras. Using machine learning, we created a convolutional neural network that accurately predicts wing motion from the activity of the steering muscles, and an encoder-decoder that predicts the role of the individual sclerites on wing motion. By replaying patterns of wing motion on a dynamically scaled robotic fly, we quantified the effects of steering muscle activity on aerodynamic forces. A physics-based simulation incorporating our hinge model generates flight manoeuvres that are remarkably similar to those of free-flying flies. This integrative, multi-disciplinary approach reveals the mechanical control logic of the insect wing hinge, arguably among the most sophisticated and evolutionarily important skeletal structures in the natural world.

Multiplexed protein imaging enables spatially resolved analysis of molecular organization in tissues, but existing spatial proteomics platforms remain constrained in scalability, throughput, and integration with RNA measurements and interpretable computational analysis. Here, we present an integrated spatial omics framework that combines highly multiplexed protein and RNA imaging with explainable machine learning to map cell-type-specific molecular and structural architectures at tissue scale. Using this platform, we simultaneously profiled up to 46 proteins and 79 RNA species across \~370,000 cells in intact mouse brain tissue at diffraction-limited subcellular resolution (\~260 nm). We developed a scalable, open-source computational pipeline for large-scale image processing and analysis, and show that nuclear protein and chromatin features alone are sufficient to accurately classify brain cell types and their spatial organization. Incorporation of explainable deep learning further enabled identification of human-interpretable, cell-type-specific subnuclear structural features directly from imaging data, with independent quantitative validation. Together, this integrated experimental and computational framework enables tissue-scale spatial proteomics-based cell-type classification and structural feature discovery, providing a broadly applicable platform for mechanistic studies, high-content screening, and translational applications.

Recent developments in machine vision methods for automatic, quantitative analysis of social behavior have immensely improved both the scale and level of resolution with which we can dissect interactions between members of the same species. In this paper, we review these methods, with a particular focus on how biologists can apply them to their own work. We discuss several components of machine vision-based analyses: methods to record high-quality video for automated analyses, video-based tracking algorithms for estimating the positions of interacting animals, and machine learning methods for recognizing patterns of interactions. These methods are extremely general in their applicability, and we review a subset of successful applications of them to biological questions in several model systems with very different types of social behaviors.

Calreticulin (CALR) is primarily an endoplasmic reticulum chaperone protein that also plays a key role in facilitating programmed cell removal (PrCR) by acting as an "eat-me" signal for macrophages, directing their recognition and engulfment of dying, diseased, or unwanted cells. Recent findings have demonstrated that macrophages can transfer their own CALR onto exposed asialoglycans on target cells, marking them for PrCR. Despite the critical role CALR plays in this process, the molecular mechanisms behind its secretion by macrophages and the formation of binding sites on target cells remain unclear. Our findings show that CALR undergoes C-terminal cleavage upon secretion, producing a truncated form that functions as the active eat-me signal detectable on target cells. We identify cathepsins as potential proteases involved in this cleavage process. Furthermore, we demonstrate that macrophages release neuraminidases, which modify the surface of target cells and facilitate CALR binding. These insights reveal a coordinated mechanism through which lipopolysaccharide (LPS)-activated macrophages regulate CALR cleavage and neuraminidase activity to mark target cells for PrCR. How they recognize the cells to be targeted remains unknown.

Pathogen-mediated activation of macrophages arms innate immune responses that include enhanced surface ruffling and macropinocytosis for environmental sampling and receptor internalization and signaling. Activation of macrophages with bacterial lipopolysaccharide (LPS) generates prominent dorsal ruffles, which are precursors for macropinosomes. Very rapid, high-resolution imaging of live macrophages with lattice light sheet microscopy (LLSM) reveals new features and actions of dorsal ruffles, which redefine the process of macropinosome formation and closure. We offer a new model in which ruffles are erected and supported by F-actin tent poles that cross over and twist to constrict the forming macropinosomes. This process allows for formation of large macropinosomes induced by LPS. We further describe the enrichment of active Rab13 on tent pole ruffles and show that CRISPR deletion of Rab13 results in aberrant tent pole ruffles and blocks the formation of large LPS-induced macropinosomes. Based on the exquisite temporal and spatial resolution of LLSM, we can redefine the ruffling and macropinosome processes that underpin innate immune responses.

A 60-year-old man diagnosed with macular telangiectasia type 1 (MacTel 1) was treated for 3 years with monthly aflibercept (Eylea; Regeneron, Tarrytown, NY) and serially imaged with spectral-domain optical coherence tomography. When administered monthly, aflibercept appeared to have a beneficial effect on macular edema secondary to MacTel 1. Visual acuity preservation despite minimal chronic macular edema could be attributed to the lack of significant photoreceptor disruption.