Pixel and superpixel classifiers have become essential tools for EM segmentation algorithms. Training these classifiers remains a major bottleneck primarily due to the requirement of completely annotating the dataset which is tedious, error-prone and costly. In this paper, we propose an interactive learning scheme for the superpixel classifier for EM segmentation. Our algorithm is "active semi-supervised" because it requests the labels of a small number of examples from user and applies label propagation technique to generate these queries. Using only a small set (<20%) of all datapoints, the proposed algorithm consistently generates a classifier almost as accurate as that estimated from a complete groundtruth. We provide segmentation results on multiple datasets to show the strength of these classifiers.

Overcoming the silencing of the fetal γ-globin gene has been a long standing goal in the treatment of sickle cell disease (SCD). The major transcriptional enhancer of the β-globin locus, called LCR, dynamically interacts with the developmental stage-appropriate β-type globin genes via chromatin looping, a process requiring the protein Ldb1. In adult erythroid cells the LCR can be re-directed from the adult β- to the fetal γ-globin promoter by tethering Ldb1 to the human γ-globin promoter with custom designed zinc finger proteins (ZF-Ldb1), leading to reactivation of γ-globin gene expression. To compare this approach to pharmacological reactivation of fetal hemoglobin (HbF), hematopoietic cells from SCD patients were treated with a lentivirus expressing the ZF-Ldb1 or with chemical HbF inducers. The HbF increase in cells treated with ZF-Ldb1 was more than double of that observed with decitabine and pomalidomide; butyrate had an intermediate effect while tranylcypromine and hydroxyurea showed relatively low HbF reactivation. ZF-Ldb1 showed comparatively little toxicity, and reduced sickle Hb (HbS) synthesis as well as sickling of SCD erythroid cells under hypoxic conditions. The efficacy and low cytotoxicity of lentiviral-mediated ZF-Ldb1 gene transfer compared to the drug regimens support its therapeutic potential for the treatment of SCD.

Actin assembly and inward flow in the plane of the immunological synapse (IS) drives the centralization of T cell receptor microclusters (TCR MCs) and the integrin leukocyte functional antigen 1 (LFA-1). Using structured-illumination microscopy (SIM), we show that actin arcs populating the medial, lamella-like region of the IS arise from linear actin filaments generated by one or more formins present at the IS distal edge. After traversing the outer, Arp2/3-generated, lamellipodia-like region of the IS, these linear filaments are organized by myosin II into antiparallel concentric arcs. Three-dimensional SIM shows that active LFA-1 often aligns with arcs, whereas TCR MCs commonly reside between arcs, and total internal reflection fluorescence SIM shows TCR MCs being swept inward by arcs. Consistently, disrupting actin arc formation via formin inhibition results in less centralized TCR MCs, missegregated integrin clusters, decreased T-B cell adhesion, and diminished TCR signaling. Together, our results define the origin, organization, and functional significance of a major actomyosin contractile structure at the IS that directly propels TCR MC transport.

Dopaminergic neurons serve multiple functions, including reinforcement processing during associative learning [1-12]. It is thus warranted to understand which dopaminergic neurons mediate which function. We study larval Drosophila, in which only approximately 120 of a total of 10,000 neurons are dopaminergic, as judged by the expression of tyrosine hydroxylase (TH), the rate-limiting enzyme of dopamine biosynthesis [5, 13]. Dopaminergic neurons mediating reinforcement in insect olfactory learning target the mushroom bodies, a higher-order "cortical" brain region [1-5, 11, 12, 14, 15]. We discover four previously undescribed paired neurons, the primary protocerebral anterior medial (pPAM) neurons. These neurons are TH positive and subdivide the medial lobe of the mushroom body into four distinct subunits. These pPAM neurons are acutely necessary for odor-sugar reward learning and require intact TH function in this process. However, they are dispensable for aversive learning and innate behavior toward the odors and sugars employed. Optogenetical activation of pPAM neurons is sufficient as a reward. Thus, the pPAM neurons convey a likely dopaminergic reward signal. In contrast, DL1 cluster neurons convey a corresponding punishment signal [5], suggesting a cellular division of labor to convey dopaminergic reward and punishment signals. On the level of individually identified neurons, this uncovers an organizational principle shared with adult Drosophila and mammals [1-4, 7, 9, 10] (but see [6]). The numerical simplicity and connectomic tractability of the larval nervous system [16-19] now offers a prospect for studying circuit principles of dopamine function at unprecedented resolution.

Cell plate formation during cytokinesis entails multiple stages occurring concurrently and requiring orchestrated vesicle delivery, membrane remodelling, and timely deposition of polysaccharides, such as callose. Understanding such a dynamic process requires dissection in time and space; this has been a major hurdle in studying cytokinesis. Using lattice light sheet microscopy (LLSM), we studied cell plate development in four dimensions, through the behavior of yellow fluorescent protein (YFP)-tagged cytokinesis-specific GTPase RABA2a vesicles. We monitored the entire duration of cell plate development, from its first emergence, with the aid of YFP-RABA2a, in both the presence and absence of cytokinetic callose. By developing a robust cytokinetic vesicle volume analysis pipeline, we identified distinct behavioral patterns, allowing the identification of three easily trackable cell plate developmental phases. Notably, the phase transition between phase I and phase II is striking, indicating a switch from membrane accumulation to the recycling of excess membrane material. We interrogated the role of callose using pharmacological inhibition with LLSM and electron microscopy. Loss of callose inhibited the phase transitions, establishing the critical role and timing of the polysaccharide deposition in cell plate expansion and maturation. This study exemplifies the power of combining LLSM with quantitative analysis to decode and untangle such a complex process.

Cell plate formation during cytokinesis entails multiple stages occurring concurrently and requiring orchestrated vesicle delivery, membrane remodeling, and timely polysaccharide deposition, such as callose. Understanding such a dynamic process requires dissection in time and space; this has been a major hurdle in studying cytokinesis. Using lattice light sheet microscopy (LLSM) we studied cell plate development in four dimensions, through the behavior of the cytokinesis specific GTPase YFP-RABA2a vesicles. We monitored the entire length of cell plate development, from its first emergence, with the aid of YFP-RABA2a, both in the presence and absence of cytokinetic callose. By developing a robust cytokinetic vesicle volume analysis pipeline, we identified distinct behavioral patterns, allowing the identification of three easily trackable, cell plate developmental phases. Notably, the phase transition between phase I and phase II is striking, indicating a switch from membrane accumulation to the recycling of excess membrane material. We interrogated the role of callose using pharmacological inhibition with LLSM and electron microscopy. Loss of callose inhibited the phase transitions, establishing the critical role and timing of the polysaccharide deposition in cell plate expansion and maturation. This study exemplifies the power of combining LLSM with quantitative analysis to decode and untangle such a complex process.

High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. While wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement, and slow when serially mapping spatially varying aberrations across large fields of view. Here, we introduce AOViFT (Adaptive Optical Vision Fourier Transformer)---a machine learning-based aberration sensing framework built around a 3D multistage Vision Transformer that operates on Fourier domain embeddings. AOViFT infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time, and memory footprint compared to conventional architectures or real-space networks. We validated AOViFT on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or post-acquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, AOViFT lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples.

Differentiable simulations of optical systems can be combined with deep learning-based reconstruction networks to enable high performance computational imaging via end-to-end (E2E) optimization of both the optical encoder and the deep decoder. This has enabled imaging applications such as 3D localization microscopy, depth estimation, and lensless photography via the optimization of local optical encoders. More challenging computational imaging applications, such as 3D snapshot microscopy which compresses 3D volumes into single 2D images, require a highly non-local optical encoder. We show that existing deep network decoders have a locality bias which prevents the optimization of such highly non-local optical encoders. We address this with a decoder based on a shallow neural network architecture using global kernel Fourier convolutional neural networks (FourierNets). We show that FourierNets surpass existing deep network based decoders at reconstructing photographs captured by the highly non-local DiffuserCam optical encoder. Further, we show that FourierNets enable E2E optimization of highly non-local optical encoders for 3D snapshot microscopy. By combining FourierNets with a large-scale multi-GPU differentiable optical simulation, we are able to optimize non-local optical encoders 170× to 7372× larger than prior state of the art, and demonstrate the potential for ROI-type specific optical encoding with a programmable microscope.

In electron crystallography, membrane protein structure is determined from two-dimensional crystals where the protein is embedded in a membrane. Once large and well-ordered 2D crystals are grown, one of the bottlenecks in electron crystallography is the collection of image data to directly provide experimental phases to high resolution. Here, we describe an approach to bypass this bottleneck, eliminating the need for high-resolution imaging. We use the strengths of electron crystallography in rapidly obtaining accurate experimental phase information from low-resolution images and accurate high-resolution amplitude information from electron diffraction. The low-resolution experimental phases were used for the placement of α helix fragments and extended to high resolution using phases from the fragments. Phases were further improved by density modifications followed by fragment expansion and structure refinement against the high-resolution diffraction data. Using this approach, structures of three membrane proteins were determined rapidly and accurately to atomic resolution without high-resolution image data.

SUMMARY: Here, we propose Fourier ring correlation-based quality estimation (FRC-QE) as a new metric for automated image quality estimation in 3D fluorescence microscopy acquisitions of cleared organoids that yields comparable measurements across experimental replicates, clearing protocols and works for different microscopy modalities.

AVAILABILITY AND IMPLEMENTATION: FRC-QE is written in ImgLib2/Java and provided as an easy-to-use and macro-scriptable plugin for Fiji. Code, documentation, sample images and further information can be found under https://github.com/PreibischLab/FRC-QE.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.