Monitoring GABAergic inhibition in the nervous system has been enabled by development of an intensiometric molecular sensor that directly detects GABA. However, the first generation iGABASnFR exhibits low signal-to-noise and suboptimal kinetics, making in vivo experiments challenging. To improve sensor performance, we targeted several sites in the protein for near-saturation mutagenesis and evaluated the resulting sensor variants in a high throughput screening system using evoked synaptic release in primary cultured neurons. This identified a sensor variant, iGABASnFR2, with 4.2-fold improved sensitivity and 20% faster kinetics, and binding affinity that remained in a range sensitive to changes in GABA concentration at synapses. We also identified sensors with an inverted response, decreasing fluorescence intensity upon GABA binding. We termed the best such negative-going sensor iGABASnFR2n, which can be used to corroborate observations with the positive-going sensor. These improvements yielded a qualitative enhancement of in vivo performance when compared directly to the original sensor. iGABASnFR2 enabled the first measurements of direction-selective GABA release in the retina. In vivo imaging in somatosensory cortex revealed that iGABASnFR2 can report volume-transmitted GABA release following whisker stimulation. Overall, the improved sensitivity and kinetics of iGABASnFR2 make it a more effective tool for imaging GABAergic transmission in intact neural circuits.

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).

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).

Calcium imaging with miniature endoscopes has become an essential tool in neuroscience, but conventional miniscopes typically record signals from only a single calcium indicator. Here, we present a dual-color miniature endoscope (miniscope) that enables simultaneous calcium imaging from two neuronal populations using spectrally distinct genetically encoded indicators. In freely moving mice, we used this system to record activity from striatal neurons of the direct (dSPN) and indirect (iSPN) pathways. We showed that dSPNs were activated earlier than iSPNs during contraversive movements, with dSPNs preferentially active during acceleration and iSPNs during deceleration. During ipsiversive turns, however, this temporal relationship was reversed. These findings indicate that dSPNs and iSPNs are not concurrently active, but instead exhibit complementary, direction-dependent dynamics that govern movement velocity. Our dual-color miniscope provides a compact, cost-effective platform for simultaneous two-population imaging, offering new opportunities to dissect coordinated activity across neural circuits in freely behaving animals.

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.

Spectral information plays a crucial role in biological imaging, yet conventional epifluorescence and histological techniques often rely on RGB image acquisition, limiting the resolution of spectrally overlapping components. Here, we present a phasor-based spectral analysis framework adapted for RGB images, enabling unsupervised segmentation and unmixing without the need for hyperspectral systems or sequential acquisition. By applying a discrete Fourier transform to the red, green, and blue intensities at each pixel, we generate a two-dimensional phasor plot where spectral relationships are encoded in modulation and phase. We demonstrate the utility of this approach across three distinct applications: segmentation of lung histology images stained with hematoxylin and eosin to quantify alveolar collapse, analysis of autofluorescence in skin lesions (nevi and melanoma) to highlight pathological spectral signatures, and spectral unmixing in multicolor-labeled U2OS cells to resolve overlapping fluorophores. Our method improves signal separation, reduces noise, and enhances biological interpretability using standard RGB acquisition. These findings establish RGB phasor analysis as a practical and powerful tool for spectral decomposition and segmentation in microscopy, bridging the gap between conventional imaging and advanced spectral analysis.

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.