MOTIVATION: Serial section microscopy is an established method for detailed anatomy reconstruction of biological specimen. During the last decade, high resolution electron microscopy (EM) of serial sections has become the de-facto standard for reconstruction of neural connectivity at ever increasing scales (EM connectomics). In serial section microscopy, the axial dimension of the volume is sampled by physically removing thin sections from the embedded specimen and subsequently imaging either the block-face or the section series. This process has limited precision leading to inhomogeneous non-planar sampling of the axial dimension of the volume which, in turn, results in distorted image volumes. This includes that section series may be collected and imaged in unknown order.

RESULTS: We developed methods to identify and correct these distortions through image-based signal analysis without any additional physical apparatus or measurements. We demonstrate the efficacy of our methods in proof of principle experiments and application to real world problems.

AVAILABILITY AND IMPLEMENTATION: We made our work available as libraries for the ImageJ distribution Fiji and for deployment in a high performance parallel computing environment. Our sources are open and available at http://github.com/saalfeldlab/section-sort, http://github.com/saalfeldlab/z-spacing and http://github.com/saalfeldlab/z-spacing-spark CONTACT: : saalfelds@janelia.hhmi.orgSupplementary information: Supplementary data are available at Bioinformatics online.

Genome-wide CRISPR screens have transformed our ability to systematically interrogate human gene function, but are currently limited to a subset of cellular phenotypes. We report a novel pooled screening approach for a wider range of cellular and subtle subcellular phenotypes. Machine learning and convolutional neural network models are trained on the subcellular phenotype to be queried. Genome-wide screening then utilizes cells stably expressing dCas9-KRAB (CRISPRi), photoactivatable fluorescent protein (PA-mCherry), and a lentiviral guide RNA (gRNA) pool. Cells are screened by using microscopy and classified by artificial intelligence (AI) algorithms, which precisely identify the genetically altered phenotype. Cells with the phenotype of interest are photoactivated and isolated via flow cytometry, and the gRNAs are identified by sequencing. A proof-of-concept screen accurately identified PINK1 as essential for Parkin recruitment to mitochondria. A genome-wide screen identified factors mediating TFEB relocation from the nucleus to the cytosol upon prolonged starvation. Twenty-one of the 64 hits called by the neural network model were independently validated, revealing new effectors of TFEB subcellular localization. This approach, AI-photoswitchable screening (AI-PS), offers a novel screening platform capable of classifying a broad range of mammalian subcellular morphologies, an approach largely unattainable with current methodologies at genome-wide scale.

We present STIM, an imaging-based computational framework for exploring, visualizing, and processing high-throughput spatial sequencing datasets. STIM is built on the powerful ImgLib2, N5 and BigDataViewer (BDV) frameworks enabling transfer of computer vision techniques to datasets with irregular measurement-spacing and arbitrary spatial resolution, such as spatial transcriptomics data generated by multiplexed targeted hybridization or spatial sequencing technologies. We illustrate STIM’s capabilities by representing, visualizing, and automatically registering publicly available spatial sequencing data from 14 serial sections of mouse brain tissue.

The brain represents sensory information in the coordinated activity of neuronal ensembles. Although the microcircuits underlying olfactory processing are well characterized in Drosophila, no studies to date have examined the encoding of odor identity by populations of neurons and related it to the odor specificity of olfactory behavior. Here we used two-photon Ca(2+) imaging to record odor-evoked responses from >100 neurons simultaneously in the Drosophila mushroom body (MB). For the first time, we demonstrate quantitatively that MB population responses contain substantial information on odor identity. Using a series of increasingly similar odor blends, we identified conditions in which odor discrimination is difficult behaviorally. We found that MB ensemble responses accounted well for olfactory acuity in this task. Kenyon cell ensembles with as few as 25 cells were sufficient to match behavioral discrimination accuracy. Using a generalization task, we demonstrated that the MB population code could predict the flies' responses to novel odors. The degree to which flies generalized a learned aversive association to unfamiliar test odors depended upon the relative similarity between the odors' evoked MB activity patterns. Discrimination and generalization place different demands on the animal, yet the flies' choices in these tasks were reliably predicted based on the amount of overlap between MB activity patterns. Therefore, these different behaviors can be understood in the context of a single physiological framework.

The automated tape-collecting ultramicrotome (ATUM) makes it possible to collect large numbers of ultrathin sections quickly-the equivalent of a petabyte of high resolution images each day. However, even high throughput image acquisition strategies generate images far more slowly (at present ~1 terabyte per day). We therefore developed WaferMapper, a software package that takes a multi-resolution approach to mapping and imaging select regions within a library of ultrathin sections. This automated method selects and directs imaging of corresponding regions within each section of an ultrathin section library (UTSL) that may contain many thousands of sections. Using WaferMapper, it is possible to map thousands of tissue sections at low resolution and target multiple points of interest for high resolution imaging based on anatomical landmarks. The program can also be used to expand previously imaged regions, acquire data under different imaging conditions, or re-image after additional tissue treatments.

This chapter describes many of the technologies, which have the potential to provide new insights into fundamental aspects of liver biology. Imaging live liver tissue in an animal with multiphoton microscopy coupled with photoactivatable fluorescent proteins and/or additional fluorescent proteins could be used to follow the lineage and fates of individual transplanted stem cells or developing transgenic cells in liver. Proteins or other molecules are labeled with a dye that can be excited with light source. Cells and proteins are generally too small to detect with the naked eye, relatively transparent when imaged by light microscopy, and are highly dynamic. With the increased signal to noise, isotropic and volumetric imaging and high speeds lattice light sheet allows for 3D super‐resolution microscopy, as well. Photomultiplier tubes, while capable of detecting and counting single photons, are less useful for high‐speed imaging because they normally only detect a single pixel at a time.

Many biomolecules in cells can be visualized with high sensitivity and specificity by fluorescence microscopy. However, the resolution of conventional light microscopy is limited by diffraction to ~200-250nm laterally and >500nm axially. Here, we describe superresolution methods based on single-molecule localization analysis of photoswitchable fluorophores (PALM: photoactivated localization microscopy) as well as our recent three-dimensional (3D) method (iPALM: interferometric PALM) that allows imaging with a resolution better than 20nm in all three dimensions. Considerations for their implementations, applications to multicolor imaging, and a recent development that extend the imaging depth of iPALM to ~750nm are discussed. As the spatial resolution of superresolution fluorescence microscopy converges with that of electron microscopy (EM), direct imaging of the same specimen using both approaches becomes feasible. This could be particularly useful for cross validation of experiments, and thus, we also describe recent methods that were developed for correlative superresolution fluorescence and EM.

Widefield imaging of calcium dynamics is an emerging method for mapping regional neural activity but is currently limited to restrained animals. Here we describe cScope, a head-mounted widefield macroscope developed to image large-scale cortical dynamics in rats during natural behavior. cScope provides a 7.8 × 4 mm field of view and dual illumination paths for both fluorescence and hemodynamic correction and can be fabricated at low cost using readily attainable components. We also report the development of Thy-1 transgenic rat strains with widespread neuronal expression of the calcium indicator GCaMP6f. We combined these two technologies to image large-scale calcium dynamics in the dorsal neocortex during a visual evidence accumulation task. Quantitative analysis of task-related dynamics revealed multiple regions having neural signals that encode behavioral choice and sensory evidence. Our results provide a new transgenic resource for calcium imaging in rats and extend the domain of head-mounted microscopes to larger-scale cortical dynamics.

Many eukaryotic transcription factors (TFs) contain intrinsically disordered low-complexity domains (LCDs), but how they drive transactivation remains unclear. Here, live-cell single-molecule imaging reveals that TF-LCDs form local high-concentration interaction hubs at synthetic and endogenous genomic loci. TF-LCD hubs stabilize DNA binding, recruit RNA polymerase II (Pol II), and activate transcription. LCD-LCD interactions within hubs are highly dynamic, display selectivity with binding partners, and are differentially sensitive to disruption by hexanediols. Under physiological conditions, rapid and reversible LCD-LCD interactions occur between TFs and the Pol II machinery without detectable phase separation. Our findings reveal fundamental mechanisms underpinning transcriptional control and suggest a framework for developing single-molecule imaging screens for novel drugs targeting gene regulatory interactions implicated in disease.