Low-dosage Maximum-A-Posteriori Focusing and Stigmation

DeepFocus: fast focus and astigmatism correction for electron microscopy

High-throughput 2D and 3D scanning electron microscopy, which relies on automation and dependable control algorithms, requires high image quality with minimal human intervention. Classical focus and astigmatism correction algorithms attempt to explicitly model image formation and subsequently aberration correction. Such models often require parameter adjustments by experts when deployed to new microscopes, challenging samples, or imaging conditions to prevent unstable convergence, making them hard to use in practice or unreliable. Here, we introduce DeepFocus, a purely data-driven method for aberration correction in scanning electron microscopy. DeepFocus works under very low signal-to-noise ratio conditions, reduces processing times by more than an order of magnitude compared to the state-of-the-art method, rapidly converges within a large aberration range, and is easily recalibrated to different microscopes or challenging samples.

Generalized spot auto-focusing method with a high-definition auto-correlation function in transmission electron microscopy

The spot auto-focusing (AF) method with a unique high-definition auto-correlation function (HD-ACF) proposed in the previous paper is improved and is now applicable to general specimens at a wide range of magnifications. According to the definition where the AF is defocused to obtain the highest resolution, the proposed method achieves the sharpest HD-ACF profile in the AF spot image. The relationship where the sharpest HD-ACF profile gives the highest resolution is theoretically explained, and practical AF examples for different specimens and magnifications are experimentally demonstrated. Specimens include a yeast cell thin section at 10-k magnification, a standard grating replica used as a ruler at 50-k, a crystal lattice of graphitized carbon at 400-k and a 60°-tilted thin section (yeast cell) at 10-k. Different procedures are prepared to actively identify the defocus position that gives the sharpest HD-ACF profile. Every AF result demonstrates the highest-resolution image.

Multi-Beam Scanning Electron Microscopy for High-Throughput Imaging in Connectomics Research

Major progress has been achieved in recent years in three-dimensional microscopy techniques. This applies to the life sciences in general, but specifically the neuroscientific field has been a main driver for developments regarding volume imaging. In particular, scanning electron microscopy offers new insights into the organization of cells and tissues by volume imaging methods, such as serial section array tomography, serial block-face imaging or focused ion beam tomography. However, most of these techniques are restricted to relatively small tissue volumes due to the limited acquisition throughput of most standard imaging techniques. Recently, a novel multi-beam scanning electron microscope technology optimized to the imaging of large sample areas has been developed. Complemented by the commercialization of automated sample preparation robots, the mapping of larger, cubic millimeter range tissue volumes at high-resolution is now within reach. This Mini Review will provide a brief overview of the various approaches to electron microscopic volume imaging, with an emphasis on serial section array tomography and multi-beam scanning electron microscopic imaging.

SBEMimage: Versatile Acquisition Control Software for Serial Block-Face Electron Microscopy

We present SBEMimage, an open-source Python-based application to operate serial block-face electron microscopy (SBEM) systems. SBEMimage is designed for complex, challenging acquisition tasks, such as large-scale volume imaging of neuronal tissue or other biological ultrastructure. Advanced monitoring, process control, and error handling capabilities improve reliability, speed, and quality of acquisitions. Debris detection, autofocus, real-time image inspection, and various other quality control features minimize the risk of data loss during long-term acquisitions. Adaptive tile selection allows for efficient imaging of large tissue volumes of arbitrary shape. The software’s graphical user interface is optimized for remote operation. In its user-friendly viewport, tile grids covering the region of interest to be acquired are overlaid on previously acquired overview images of the sample surface. Images from other sources, e.g., light microscopes, can be imported and superimposed. SBEMimage complements existing DigitalMicrograph (Gatan Microscopy Suite) installations on 3View systems but permits higher acquisition rates by interacting directly with the microscope’s control software. Its modular architecture and the use of Python/PyQt make SBEMimage highly customizable and extensible, which allows for fast prototyping and will permit adaptation to a wide range of SBEM systems and applications.

Spot auto-focusing and spot auto-stigmation methods with high-definition auto-correlation function in high-resolution TEM

As alternatives to the diffractogram-based method in high-resolution transmission electron microscopy, a spot auto-focusing (AF) method and a spot auto-stigmation (AS) method are presented with a unique high-definition auto-correlation function (HD-ACF). The HD-ACF clearly resolves the ACF central peak region in small amorphous-thin-film images, reflecting the phase contrast transfer function. At a 300-k magnification for a 120-kV transmission electron microscope, the smallest areas used are 64 × 64 pixels (~3 nm2) for the AF and 256 × 256 pixels for the AS. A useful advantage of these methods is that the AF function has an allowable accuracy even for a low s/n (~1.0) image. A reference database on the defocus dependency of the HD-ACF by the pre-acquisition of through-focus amorphous-thin-film images must be prepared to use these methods. This can be very beneficial because the specimens are not limited to approximations of weak phase objects but can be extended to objects outside such approximations.

Enhanced FIB-SEM systems for large-volume 3D imaging

Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) can automatically generate 3D images with superior z-axis resolution, yielding data that needs minimal image registration and related post-processing. Obstacles blocking wider adoption of FIB-SEM include slow imaging speed and lack of long-term system stability, which caps the maximum possible acquisition volume. Here, we present techniques that accelerate image acquisition while greatly improving FIB-SEM reliability, allowing the system to operate for months and generating continuously imaged volumes > 10⁶ µm³. These volumes are large enough for connectomics, where the excellent z resolution can help in tracing of small neuronal processes and accelerate the tedious and time-consuming human proofreading effort. Even higher resolution can be achieved on smaller volumes. We present example data sets from mammalian neural tissue, Drosophila brain, and Chlamydomonas reinhardtii to illustrate the power of this novel high-resolution technique to address questions in both connectomics and cell biology.

DOI:http://dx.doi.org/10.7554/eLife.25916.001

Precise three-dimensional imaging can help make sense of microscopic details in biology. These images are usually built up from many two-dimensional images stacked on top of each other. One approach for examining particularly fine details, such as the connections between nerve cells in the brain, is called focused ion beam scanning electron microscopy (or FIB-SEM for short). This approach works by creating an image of the surface layer of a sample, which is then stripped away using a beam of charged particles to reveal the layer beneath. The new surface can then be imaged and so on, through the whole sample.

Unfortunately, FIB-SEM devices are currently slow and can only run for a short time, leading to a lack of continuity in the stack of images. FIB-SEM would allow faster, more accurate and detailed studies of connections between brain cells, and other elaborate biological systems, if the technology could be made faster and more reliable over months of continuous operation. The current technical challenge is to create a system that can, for example, successfully image and analyse all the connections between the more than 100 thousand cells that make up the brain of a fruit fly – a common model organism in neurobiology.

Xu et al. aimed to create a technique to image a complete fly brain, with gaps of just 8 nanometres between each image in a stack, within a reasonable timeframe. By improving how FIB-SEM signals are detected, making use of advances in ion beam controls, and by engineering ways to recover from system malfunctions, Xu et al. developed an enhanced FIB-SEM device. To demonstrate its value, the new technology was used to create images of a third of a fruit fly’s brain, parts of a mouse’s brain, and cells of a single-celled alga called Chlamydomonas reinhardtii.

The results show that large and complex samples can be successfully imaged in their entirety to adequate detail, enabling high-quality reconstruction of the connections between nerve cells. The level of detail, which can be further increased for smaller samples, offers advantages in precision and image quality over other comparable techniques. As well as helping to study the brain, this approach could also be used to examine details inside cells. Future work to advance this technology will enable larger and more complete imaging of elaborate biological structures.

DOI:http://dx.doi.org/10.7554/eLife.25916.002

Challenges of microtome-based serial block-face scanning electron microscopy in neuroscience

Serial block-face scanning electron microscopy (SBEM) is becoming increasingly popular for a wide range of applications in many disciplines from biology to material sciences. This review focuses on applications for circuit reconstruction in neuroscience, which is one of the major driving forces advancing SBEM. Neuronal circuit reconstruction poses exceptional challenges to volume EM in terms of resolution, field of view, acquisition time and sample preparation. Mapping the connections between neurons in the brain is crucial for understanding information flow and information processing in the brain. However, information on the connectivity between hundreds or even thousands of neurons densely packed in neuronal microcircuits is still largely missing. Volume EM techniques such as serial section TEM, automated tape-collecting ultramicrotome, focused ion-beam scanning electron microscopy and SBEM (microtome serial block-face scanning electron microscopy) are the techniques that provide sufficient resolution to resolve ultrastructural details such as synapses and provides sufficient field of view for dense reconstruction of neuronal circuits. While volume EM techniques are advancing, they are generating large data sets on the terabyte scale that require new image processing workflows and analysis tools. In this review, we present the recent advances in SBEM for circuit reconstruction in neuroscience and an overview of existing image processing and analysis pipelines.

High-resolution whole-brain staining for electron microscopic circuit reconstruction

Currently only electron microscopy provides the resolution necessary to reconstruct neuronal circuits completely and with single-synapse resolution. Because almost all behaviors rely on neural computations widely distributed throughout the brain, a reconstruction of brain-wide circuits-and, ultimately, the entire brain-is highly desirable. However, these reconstructions require the undivided brain to be prepared for electron microscopic observation. Here we describe a preparation, BROPA (brain-wide reduced-osmium staining with pyrogallol-mediated amplification), that results in the preservation and staining of ultrastructural details throughout the brain at a resolution necessary for tracing neuronal processes and identifying synaptic contacts between them. Using serial block-face electron microscopy (SBEM), we tested human annotator ability to follow neural 'wires' reliably and over long distances as well as the ability to detect synaptic contacts. Our results suggest that the BROPA method can produce a preparation suitable for the reconstruction of neural circuits spanning an entire mouse brain.

Connectomic reconstruction of the inner plexiform layer in the mouse retina

Comprehensive high-resolution structural maps are central to functional exploration and understanding in biology. For the nervous system, in which high resolution and large spatial extent are both needed, such maps are scarce as they challenge data acquisition and analysis capabilities. Here we present for the mouse inner plexiform layer--the main computational neuropil region in the mammalian retina--the dense reconstruction of 950 neurons and their mutual contacts. This was achieved by applying a combination of crowd-sourced manual annotation and machine-learning-based volume segmentation to serial block-face electron microscopy data. We characterize a new type of retinal bipolar interneuron and show that we can subdivide a known type based on connectivity. Circuit motifs that emerge from our data indicate a functional mechanism for a known cellular response in a ganglion cell that detects localized motion, and predict that another ganglion cell is motion sensitive.