Quality assessment of tree-like structures obtained from a neuron reconstruction algorithm is necessary for evaluating the performance of the algorithm. The lack of user-friendly software for calculating common metrics motivated us to develop a Python toolbox called PyNeval, which is the first open-source toolbox designed to evaluate reconstruction results conveniently as far as we know. The toolbox supports popular metrics in two major categories, geometrical metrics and topological metrics, with an easy way to configure custom parameters for each metric. We tested the toolbox on both synthetic data and real data to show its reliability and robustness. As a demonstration of the toolbox in real applications, we used the toolbox to improve the performance of a tracing algorithm successfully by integrating it into an optimization procedure.

Pyramidal neurons are characterized by their distinct apical and basal dendritic trees and the pyramidal shape of their soma. They are found in several regions of the CNS and, although the reasons for their abundance remain unclear, functional studies--especially of CA1 hippocampal and layer V neocortical pyramidal neurons--have offered insights into the functions of their unique cellular architecture. Pyramidal neurons are not all identical, but some shared functional principles can be identified. In particular, the existence of dendritic domains with distinct synaptic inputs, excitability, modulation and plasticity appears to be a common feature that allows synapses throughout the dendritic tree to contribute to action-potential generation. These properties support a variety of coincidence-detection mechanisms, which are likely to be crucial for synaptic integration and plasticity.

Neurons relevant to a particular behavior are often widely dispersed across the brain. To record activity in groups of individual neurons that might be distributed across large distances, neuroscientists and optical engineers have been developing a new type of microscope called a mesoscope. Mesoscopes have high spatial resolution and a large field of view. This Q&A will discuss this exciting new technology, highlighting a particular instrument, the two-photon random access mesoscope (2pRAM).

Post-translational histone modifications are highly correlated with transcriptional activity, but the relative timing of these marks and their dynamic interplay during gene regulation remains controversial. To shed light on this problem and clarify the connections between histone modifications and transcription, we demonstrate how FabLEM (Fab-based Live Endogenous Modification labeling) can be used to simultaneously track histone H3 Lysine 9 acetylation (H3K9ac) together with RNA polymerase II Serine 2 and Serine 5 phosphorylation (RNAP2 Ser2ph/Ser5ph) in single living cells and their progeny. We provide a detailed description of the FabLEM methodology, including helpful tips for preparing and loading fluorescently conjugated antigen binding fragments (Fab) into cells for optimal results. We also introduce simple procedures for analyzing and visualizing FabLEM data, including color-coded scatterplots to track correlations between modifications through the cell cycle and temporal cross-correlation analysis to dissect modification dynamics. Using these methods, we find significant correlations that span cell generations, with a relatively strong correlation between H3K9ac and Ser5ph that appears to peak a few hours before mitosis and may reflect the bookmarking of genes for efficient re-initiation following mitosis. The techniques we have developed are broadly applicable and should help clarify how histone modifications dynamically contribute to gene regulation.

Quantifying molecular dynamics within the context of complex cellular morphologies is essential toward understanding the inner workings and function of cells. Fluorescence recovery after photobleaching (FRAP) is one of the most broadly applied techniques to measure the reaction diffusion dynamics of molecules in living cells. FRAP measurements typically restrict themselves to single-plane image acquisition within a subcellular-sized region of interest due to the limited temporal resolution and undesirable photobleaching induced by 3D fluorescence confocal or widefield microscopy. Here, an experimental and computational pipeline combining lattice light sheet microscopy, FRAP, and numerical simulations, offering rapid and minimally invasive quantification of molecular dynamics with respect to 3D cell morphology is presented. Having the opportunity to accurately measure and interpret the dynamics of molecules in 3D with respect to cell morphology has the potential to reveal unprecedented insights into the function of living cells.

Progressive, technological achievements in the quantitative fluorescence microscopy field are allowing researches from many different areas to start unraveling the dynamic intricacies of biological processes inside living cells. From super-resolution microscopy techniques to tracking of individual proteins, fluorescence microscopy is changing our perspective on how the cell works. Fortunately, a growing number of research groups are exploring single-molecule studies in living cells. However, no clear consensus exists on several key aspects of the technique such as image acquisition conditions, or analysis of the obtained data. Here, we describe a detailed approach to perform single-molecule tracking (SMT) of transcription factors in living cells to obtain key binding characteristics, namely their residence time and bound fractions. We discuss different types of fluorophores, labeling density, microscope, cameras, data acquisition, and data analysis. Using the glucocorticoid receptor as a model transcription factor, we compared alternate tags (GFP, mEOS, HaloTag, SNAP-tag, CLIP-tag) for potential multicolor applications. We also examine different methods to extract the dissociation rates and compare them with simulated data. Finally, we discuss several challenges that this exciting technique still faces.

We address the problem of explaining the decision process of deep neural network classifiers on images, which is of particular importance in biomedical datasets where class-relevant differences are not always obvious to a human observer. Our proposed solution, termed quantitative attribution with counterfactuals (QuAC), generates visual explanations that highlight class-relevant differences by attributing the classifier decision to changes of visual features in small parts of an image. To that end, we train a separate network to generate counterfactual images (i.e., to translate images between different classes). We then find the most important differences using novel discriminative attribution methods. Crucially, QuAC allows scoring of the attribution and thus provides a measure to quantify and compare the fidelity of a visual explanation. We demonstrate the suitability and limitations of QuAC on two datasets: (1) a synthetic dataset with known class differences, representing different levels of protein aggregation in cells and (2) an electron microscopy dataset of D. melanogaster synapses with different neurotransmitters, where QuAC reveals so far unknown visual differences. We further discuss how QuAC can be used to interrogate mispredictions to shed light on unexpected inter-class similarities and intra-class differences.

A new generation of direct electron detectors for transmission electron microscopy (TEM) promises significant improvement over previous detectors in terms of their modulation transfer function (MTF) and detective quantum efficiency (DQE). However, the performance of these new detectors needs to be carefully monitored in order to optimize imaging conditions and check for degradation over time. We have developed an easy-to-use software tool, FindDQE, to measure MTF and DQE of electron detectors using images of a microscope’s built-in beam stop. Using this software, we have determined the DQE curves of four direct electron detectors currently available: the Gatan K2 Summit, the FEI Falcon I and II, and the Direct Electron DE-12, under a variety of total dose and dose rate conditions. We have additionally measured the curves for the Gatan US4000 and TVIPS TemCam-F416 scintillator-based cameras. We compare the results from our new method with published curves.

Live imaging of large biological specimens is fundamentally limited by the short optical penetration depth of light microscopes. To maximize physical coverage, we developed the SiMView technology framework for high-speed in vivo imaging, which records multiple views of the specimen simultaneously. SiMView consists of a light-sheet microscope with four synchronized optical arms, real-time electronics for long-term sCMOS-based image acquisition at 175 million voxels per second, and computational modules for high-throughput image registration, segmentation, tracking and real-time management of the terabytes of multiview data recorded per specimen. We developed one-photon and multiphoton SiMView implementations and recorded cellular dynamics in entire Drosophila melanogaster embryos with 30-s temporal resolution throughout development. We furthermore performed high-resolution long-term imaging of the developing nervous system and followed neuroblast cell lineages in vivo. SiMView data sets provide quantitative morphological information even for fast global processes and enable accurate automated cell tracking in the entire early embryo.

High-resolution movies in the Digital Embryo repository
Nature News: "Fruitfly development, cell by cell" by Lauren Gravitz
Nature Methods Technology Feature: "Faster frames, clearer pictures" by Monya Baker
Andor Insight Awards: Life Sciences Winner

Glucose is an essential source of energy for the brain. Recently, the development of genetically encoded fluorescent biosensors has allowed real time visualization of glucose dynamics from individual neurons and astrocytes. A major difficulty for this approach, even for ratiometric sensors, is the lack of a practical method to convert such measurements into actual concentrations in ex vivo brain tissue or in vivo. Fluorescence lifetime imaging provides a strategy to overcome this. In a previous study, we reported the lifetime glucose sensor iGlucoSnFR-TS (then called SweetieTS) for monitoring changes in neuronal glucose levels in response to stimulation. This genetically encoded sensor was generated by combining the Thermus thermophilus glucose-binding protein with a circularly permuted variant of the monomeric fluorescent protein T-Sapphire. Here, we provide more details on iGlucoSnFR-TS design and characterization, as well as pH and temperature sensitivities. For accurate estimation of glucose concentrations, the sensor must be calibrated at the same temperature as the experiments. We find that when the extracellular glucose concentration is in the range 2-10 mM, the intracellular glucose concentration in hippocampal neurons from acute brain slices is ~20% of the nominal external glucose concentration (~0.4-2 mM). We also measured the cytosolic neuronal glucose concentration in vivo, finding a range of ~0.7-2.5 mM in cortical neurons from awake mice.