The lung, one the largest branching organs in our body and vital for survival at first breath, is commonly known as the gas-exchange organ. Recent novel findings on lung sensing cells, molecules, and lung-brain crosstalk in the emerging discipline of interoception have highlighted the consideration of the lung as a sensory organ. Acknowledging the central importance of lung sensing to human health and disease, NHLBI convened a workshop to synthesize the past, current, and future of lung sensing research, with in-person presentations by ∼20 interdisciplinary experts and a large virtual audience. Here, we highlight the key topics discussed and summarize a blueprint for the future in this important field.

Opsins are known as proteins that bind retinal to form light-sensing rhodopsins, which mediate diverse forms of photoreception and are widely used for optogenetic control of cellular activity. Yet many opsins lack canonical retinal-binding features or, in their native biological contexts, are expressed where chromophore availability is limited, raising the question whether retinal-free opsins are silent. Here we show that retinal-free ion-transporting microbial opsins are not inert but adopt intrinsic, light-independent functional states alongside their canonical light-driven activity. Using developmental phenotyping in transgenic Drosophila melanogaster, electrophysiology in Xenopus oocytes and mammalian cells, together with structural and computational analyses, we demonstrate robust light-independent ion conductance in multiple channel-type opsins and an ion-pumping opsin. Structural modeling and molecular dynamics simulations reveal conformational rearrangements in transmembrane helices and internal water networks that are consistent with stabilization of apo-conductive states. Together, these findings establish apo-opsin activity as a defined functional state of microbial opsins, expand the current framework of rhodopsin biology, and highlight the need to consider chromophore-independent activity in both basic research and optogenetic applications.

Chemically inducible expression systems enable transgene expression regulation in response to external small molecules. Tetracycline repressor (TetR)-based gene switches work in plants, but antibiotics are neither approved nor advisable for crop use. Here we report engineering of TetR mutants that respond to approved sulfonylurea (SU) herbicides instead of antibiotics. Designed variants show low-nanomolar EC values for ethametsulfuron-methyl (Es) or chlorsulfuron and tightly bind the Tet operator sequence, but only in the absence of corresponding SUs. Crystal structures of two repressors in complex with their respective SU ligands reveal extensive interactions explaining their strong binding. The Es repressor-based gene switch is introduced into tobacco, soybean, maize, rice, and Arabidopsis, and robust reporter gene activation is observed upon herbicide application. Addition of a repressor-regulated siRNA targeting the repressor transcript increases the magnitude and spatial distribution of the response following herbicide treatment and results in a partially bistable gene switch. The SU repressors also function well in mammalian cell culture and may enable regulation of additional genes in conjunction with TetR.

ust as genomes revolutionized molecular genetics, connectomes (maps of neurons and synapses) are transforming neuroscience. To date, the only organisms with complete connectomes are worms1-3, sea squirts4, and comb jellies5 (103-104 synapses). By contrast, the fruit fly is more complex (108 synaptic connections), with a brain that supports learning and spatial memory6,7 and an intricate ventral nerve cord analogous to the vertebrate spinal cord8-12. Here we report the first densely-reconstructed adult fly connectome that unites the brain and ventral nerve cord, and we leverage this resource to investigate principles of neural control. We show that effector neurons (motor neurons, endocrine cells, and efferent neurons targeting the viscera) are primarily influenced by sensory neurons in the same body part, forming local feedback loops. These local loops are linked by long-range circuits involving ascending and descending neurons organized into behavior-centric modules. Single ascending and descending neurons are often positioned to influence the voluntary movements of multiple body parts, together with the endocrine cells or visceral organs that support those movements. Brain regions involved in learning and navigation supervise these circuits. These results reveal an architecture that is distributed, parallelized, and embodied, reminiscent of distributed control architectures in engineered systems13,14.

Three dimensional fluorescence microscopy often exhibits anisotropic resolution because axial information is poorly sampled and more blurred than lateral information, which complicates quantitative interpretation of fine 3D structures. Although optical remedies and computational restoration have been explored, many approaches require demanding system calibration or rely on accurate PSF models and assumptions that are difficult to satisfy across all samples and modalities. Here we present DeepIso, a self-supervised isotropy restoration framework that couples supervised pretraining with an internal-learning inference stage to estimate degradation directly from the measured volume. Without explicit PSF specification or enforced lateral-axial structural equivalence, DeepIso recovers axial frequency content and improves the continuity of elongated structures while retaining fine features, with superior performance over existing computational approaches in terms of both visual inspection and quantitative metrics. The method is validated on synthetic benchmarks and experimental datasets, demonstrating isotropy enhancement across confocal, light-sheet, and 3D structured illumination microscopy, thereby supporting downstream volumetric analysis including segmentation and tracking.

Motion is an essential component of any living system. It is rich with information, but it is often challenging to quantitatively extract biologically informative results from the motion apparent in microscopy images. This challenge is exacerbated by the wide variety in biological movement, which often takes the form of difficult-to-segment amorphous structures undergoing complex motion. An image processing technique known as optical flow can capture motion at each pixel in an image, thus bypassing the need for object segmentation or a priori definition of motion types. This makes it a powerful tool for quantitative assessment of biological systems from the protein to organism scale. However, despite its flexibility and strengths for analyzing fluorescence microscopy images, its adoption in the bioimaging community has been limited by the availability of easy-to-use tools and guidance in results interpretation. Here we describe an optical flow tool, OpticalFlow3D, that can be run in Python or MATLAB and is compatible with three-dimensional microscopy images. Using biological examples across length scales, we illustrate how OpticalFlow3D can enable new biological insight.

High-resolution extracellular electrophysiology is the gold standard for recording spikes from distributed neural populations and is especially powerful when combined with optogenetics for manipulation of specific cell types with high temporal resolution. We integrated these approaches into prototype Neuropixels Opto probes, which combine electronic and photonic circuits. These devices pack 960 electrical recording sites and two sets of 14 light emitters onto a 70-μm-wide, 1-cm-long shank, allowing spatially addressable optogenetic stimulation with blue and red light. In mouse cortex, Neuropixels Opto probes delivered high-quality recordings together with spatially addressable optogenetics, differentially activating or silencing neurons at distinct cortical depths. In the mouse striatum and other deep structures, Neuropixels Opto probes delivered efficient optotagging, facilitating the identification of two cell types in parallel. Neuropixels Opto probes represent a promising tool for recording, identifying and manipulating neuronal populations.

Fluorescence microscopy provides insights into cellular structure and function, but the undesirable bending of light from the sample or imperfect optics (“optical aberrations”) often degrade imaging resolution or signal. Adaptive Optics (AO) techniques that sense and subsequently correct the aberrations can restore diffraction-limited imaging, but most implementations remain technically complex and expensive. Wavefront sensing, the key step in AO, remains particularly challenging due to slow speed or additional illumination dose imparted to the sample. Previously we showed that phase diversity (PD), a method for wavefront sensing originally developed for use in astronomy, can rapidly sense and correct aberrations in a widefield fluorescence microscope. Here we extend this approach to light sheet microscopy, showing that the PD-based AO of sample-induced aberrations in live Caenorhabditis elegans embryos restores image contrast and resolution, enabling subcellular imaging throughout the imaging volume. In addition to these preliminary results, the further improvement and extension of this technology to additional samples and microscopes will be discussed.

Focal cortical dysplasia (FCD) is a leading cause of drug-resistant focal epilepsy, especially in children. However, up to 30−40% of FCD lesions are “MRI-negative,” eluding visual detection on standard scans. Even expert neuroradiologists miss about one-third of these subtle lesions. Prior automated FCD detection approaches have shown promise but face important limitations. Conventional morphometry pipelines (e.g., FreeSurfer-based thickness or junction maps) rely on hand-tuned thresholds and often struggle with small cohorts and site-specific bias. Voxel-level deep learning methods, including 3D CNN and transformer models, can improve sensitivity but tend to produce excessive false positives and lack interpretability. In this work, we propose FCDNets, a graph neural network (GNN) that learns from multi-contrast MRI cortical surface maps to detect FCD lesions. Trained using only open-access MRI datasets (85 patients and 85 controls from OpenNeuro ds004199; 101 patients and 177 controls from MELD-Public), FCDNets achieves vertex-level AUROC 0.975±0.006, lesion Dice 0.64±0.12, and 82% patient sensitivity at ≤5 cm2 false-positive cortex in cross-site five-fold CV. Compared with a 3-D CNN baseline, FCDNets yields +28 percentage points higher sensitivity (82 % vs. 54 %) and 45 % fewer false-positive clusters (0.9 vs. 1.6). On MELD-Public, it maintains AUROC 0.782 and 63% sensitivity. The surface-based approach produces interpretable lesion probability maps aligned with known FCD imaging biomarkers. This open, multi-center study demonstrates the potential of surface-GNNs to aid in localizing subtle epileptogenic lesions that were previously MRIoccult, which may accelerate diagnosis and surgical treatment for drug-resistant epilepsy.

Mechanical heterogeneity and collagen topology in tumor extracellular matrix (ECM) hinder nanoparticle (NP) transport and uptake, motivating models that couple NP mechanics with measured tumor mechanics. Non-invasive stiffness mapping (e.g., MR elastography) and histology-derived stiffness inference (STIFMap) enable learning on patientproximal mechanical maps. We introduce MechGNN, a dualgraph framework with cross-attention that represents NPs (core/shell/ligands) and ECM fiber networks as interacting graphs, and embeds physics-informed edge functions derived from Hertzian contact and receptor-ligand binding kinetics to regularize message passing. Using only public re-sources-STIFMap and open SHG collagen images quantified with CT-FIRE/CurveAlign/TWOMBLI-plus open FEM/BD simulations (FEniCS; Brownian dynamics) and open NP/ligand repositories (caNanoLab, BindingDB, IUPHAR/BPS Guide to Pharmacology), MechGNN achieves a 12−16%reduction in penetration-depth RMSE relative to the strongest baseline on synthetic-from-real ECM testbeds and sustains an 8−13% RMSE gain in leave one tumor type out tests; physics losses reduce constraint violation rates by ≈35%. The pipeline provides a reproducible bridge from measured ECM mechanics to mechanically designable NP properties.