Mitochondria utilize calcium to increase ATP synthesis. However, excessive matrix calcium activates the mitochondrial permeability transition (mPT), a process that permeabilizes the mitochondrial inner membrane and leads to cell death. While initially characterized 50 y ago, the proteins underlying the process are unclear, although integral membrane proteins were expected to be the porous entities during calcium overload. Here, we designed two assays to study the mPT using high-throughput methodologies. By surveying 19,113 proteins in human cells, we identified four proteins that sensitize the human mPT, but only one that was essential for mPT activation, mitochondrial-localized NRLX1. Surprisingly, NLRX1 is not an integral membrane protein, and our work did not identify any essential integral membrane proteins for the human mPT. The mitochondrial permeability transition (mPT) is an evolutionarily conserved destructive process that permeabilizes the inner mitochondrial membrane in response to calcium overload. The molecular mechanism underlying the mPT is not established. To unambiguously identify essential proteins, we designed two phenotypic assays for mitochondrial calcium overload and applied them to FACS-based CRISPR screening in human cells, ultimately evaluating 19,113 genes. The first screen studied mitochondrial membrane potential (MMP) collapse in response to calcium overload. Top-ranked genes were the essential proteins of the mitochondrial calcium uniporter complex, MCU and EMRE, reflecting that the calcium-induced MMP collapse results from mitochondrial calcium entry and not the mPT. The second screen measured the permeability of the inner mitochondrial membrane. Here, the fluorescent interaction of a membrane impermeant 600 Da dye and a mitochondrial-targeted HaloTag protein was studied under mPT activating conditions; calcium overload and the thiol-reactive molecule phenylarsine oxide. With secondary validation, we identified four protein-encoding genes that delayed or prevented the mPT under knockout: NF2, REST, BPTF, and NRLX1. Knockout of the nonmitochondrial proteins BPTF, NF2, or REST increased mitochondrial calcium retention capacity (CRC). However, calcium release or sensitivity to cyclosporin A (CsA) persisted, indicative of mPT sensitizers. Only knockout of the mitochondrial matrix protein, NLRX1, increased CRC, abolished calcium release, and was CsA-insensitive. This top-ranked hit of the mitochondrial permeability screen meets the definition of an essential mPT activator. Integral membrane proteins, including all previously proposed mPT candidates, were not essential activators.

Most existing deep learning-based cell tracking methods rely on supervised learning, requiring large-scale annotated datasets that are often unavailable in real-world scenarios. Moreover, many approaches lack tools and methods for correcting mispredicted links or incorporating corrections through fine-tuning. These limitations contribute to the limited adoption of deep learning-based tracking methods in the life sciences, where manual tracking remains the predominant approach. To reduce the annotation burden and enable model training without extensive labeled data, we introduce a loss function for unsupervised training. Our method leverages the predictable dynamics inherent in many biological processes, providing an initialization that does not require an annotated dataset. We further investigate how minimal user-provided annotations can refine tracking accuracy. To this end, we propose an active learning framework that selectively identifies uncertain decisions within the tracking graph, allowing for efficient annotation of the most informative data points. We evaluate our approach on two microscopy datasets, demonstrating the effectiveness of both our unsupervised training strategy and active learning scheme in improving tracking performance. Our implementation and reproducible experiments are available at github.com/funkelab/attrackt and github.com/funkelab/attrackt_experiments, respectively.

Expansion microscopy (ExM) enables nanoscale imaging on standard microscopes, but combining ExM with single-molecule localization microscopy (SMLM) remains difficult, owing to the incompatibility of expanded hydrogels with photoswitching buffers. Here, we introduce a single-step expansion microscopy method that allows SMLM with spontaneously blinking dyes in 6-14× expanded samples, without re-embedding. We demonstrate nanometer-resolution imaging by resolving the organization of the nuclear pore complex (NPC) and the molecular structure of recombinant homotrimeric proliferating cell nuclear antigen (PCNA).

Many fungi utilize high turgor pressure for morphogenesis, requiring tight regulation of ionic gradients. Ion regulation is important for pathogenesis, reproduction, and general homeostasis across the fungal kingdom. In the major human fungal pathogen Candida albicans, potassium (K+) channels fine-tune ionic balance under stressful environmental conditions, contributing to colonization of the human host. Two-pore domain, outwardly rectifying potassium (TOK) channels, uniquely found in fungi, remain insufficiently characterized despite early evidence implicating them in diverse intracellular processes essential for cellular growth and viability, and their potential as antifungal targets. Here, we describe the first atomic resolution structure of a fungal potassium channel—TOK1 from C. albicans (CaTOK)—revealing a membrane topology distinct from all other known K+ channel classes. We propose that CaTOK1 utilizes two unique regions—TOK auxiliary subunit-like channel (TALC) domain and a structured c-terminal bundle—to regulate TOK1 gating. Conformational analysis of TOK1 pore features an inner helical gating mechanism with “up” and “down” conformations similar to mammalian dimeric K+ channels. These findings provide a structural framework for understanding TOK channel activity and lay the groundwork for future studies on fungal ion homeostasis, pathogenicity, and therapeutic development.

Fluorescence microscopy is a powerful tool for studying biomolecules in their native environments, offering high spatio-temporal resolution but requiring fluorescent labels. Current live-cell compatible labeling strategies repurpose natural systems, such as fluorescent proteins or proteins that bind fluorescent ligands. While advances have been made to engineer natural proteins into labels with minimal size, high brightness, as well as enhanced thermo- and photostability, these approaches often require trade-offs among desirable properties due to the inherent limitations of re-engineering natural proteins. In this work, we present rhodamine binder (Rhobin) tags—de novo-designed proteins that bind rhodamine-derived fluorophores. Unlike traditional approaches, Rhobin tags were developed by directly incorporating desirable features during the computational design process, which resulted in compact binders with outstanding thermostability. Their nanomolar substrate affinity, rapid labeling kinetics, and orthogonality to established labeling systems such as HaloTag and SNAP-tag facilitate versatile live and fixed-cell imaging of diverse subcellular targets in mammalian cells. Transient fluorophore binding further enables advanced imaging techniques, including live-cell super-resolution STED microscopy with reduced photobleaching and single-molecule localization microscopy in live and fixed cells. To the stability of Rhobin tags under extreme environmental conditions, we demonstrate showcase protein tagging and timelapse imaging in the extremophile Sulfolobus acidocaldarius living at 75°C, an application previously inaccessible with existing tags. We anticipate that Rhobin tags will become a central component in the toolbox of fluorescent labels and will pave the way for a new generation of modular protein tags and biosensors with tailor-made properties.

Potassium (K+) channels play a vital role in helping fungal pathogens like Candida albicans adapt to hostile environments within the human body, including during infection. Among these, two-pore domain, outwardly rectifying K+ (TOK) channels, unique to fungi, have remained insufficiently characterized, despite evidence that they support processes in fungi, such as ion homeostasis, growth, and virulence, highlighting their potential as antifungal targets. Here, we present the cryo-EM structure of C. albicans TOK1 (CaTOK), the first resolved structure of a TOK channel and of any fungal K+ channel. CaTOK reveals an eight-transmembrane helical fold unlike any other K+ channel structure determined to date, including a domain formed by helices S1-S4 with unexpected structural homology to channel auxiliary subunits critical for human neuronal signaling. Additionally, a unique cytosolic C-terminal domain interfaces with pore-lining helices, suggesting a role in channel regulation. These findings uncover previously unrecognized structural elements that broaden our understanding of K+channel diversity and regulation and provide initial clues into the structural basis for the unique functional attributes of the TOK family.

To form a blood clot, fibrinogen is converted into fibrin through the action of the enzyme thrombin. Fibrin then polymerizes longitudinally and laterally as it matures into a fiber. Polymerization results in a dense, 3-dimensional branched network. Previous research has shown the relevance of these fibrin gel structures in hemostatic conditions; however, the mechanism by which they form has not been fully resolved. Using light sheet microscopy, 3-dimensional volumes of the fibrin polymerization process were captured. Manual annotation of these microscopy videos revealed that fiber branch points occur through the collision and the binding of diffusing fibers rather than through the splitting of growing fiber termini. However, the density of fibers and amount of data greatly slows manual annotation-based analysis and limits the ability to capture important data, such as growth rates and fiber stiffness. To more quickly process these data, a computational approach was utilized. A custom tracking pipeline, suited to the networks formed by cylindrical fibrin fibers, was developed, beginning with an AI-based classifier. This custom pipeline allowed for the tracking of uniquely labeled fibers over time. Automated merge detection between linking phases further improved accuracy. Additionally, network formation was analyzed through skeletonization techniques to measure the number of branches per junction over time. Combining the skeletonization and tracking methods, single fibers were identified by their lack of branch points and tracked. The addition of branch points to previously tracked objects served as a signal for merge detection. This approach yielded measurements of single fibrin fiber diffusion rates, as well as the first volumetric and length growth rates of fibers throughout polymerization. In addition, the gel point was quantified by analyzing the span of connected objects to characterize the network consolidation over time at the level of single fibers.

Local field potentials (LFPs) reflect coordination among neural populations, yet their exact relationship to neural computation remains unknown. One exception is the theta rhythm of the rodent hippocampus, which organizes sequential firing among place cells, enabling spike timing to track the animal's path through its environment. But when the animal stops, the theta rhythm becomes irregular, which is assumed to disrupt its ability to carry spatial information. Here we challenge this assumption by developing an artificial neural network that discovers position-tuned theta rhythms (pThetas) from LFPs even in the absence of strong theta oscillations. Using recordings from male rats, we provide evidence that pTheta is distinct from the dominant theta rhythm, while reflecting rhythmic coordination among place cell populations. Our work suggests that weak and intermittent oscillations, as seen in many brain regions and species, can convey information commensurate with population spike codes when decoded using information-based rather than variance-based principles.

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.

Coordinated lateralized movements are critical for natural orienting behaviors, but their neural bases remain poorly understood. The deep superior colliculus (dSC) integrates a wide range of inputs to select targets for orienting movements and coordinates downstream activity to initiate and execute movement. The substantia nigra pars reticulata (SNr) is thought to disinhibit dSC to facilitate movement, but much remains unknown about the relationship between SNr activity, dSC activity, and movement. We recorded from both regions using high-density probes in head-fixed mice performing directional orienting tasks. We found that dSC and SNr activity reflected task variables preceding and throughout movement. However, the direction-dependence of dSC activity was weaker than in other orienting behaviors, and the relationship between movement-related dSC and SNr activity was inconsistent with disinhibition of dSC determining the initiation or direction of movement. Analyses of similar data curated by the International Brain Laboratory yielded consistent results. These findings suggest diverse roles for modulatory input from SNr to dSC in shaping motor behavior.