The development of single-molecule localization microscopy (SMLM) has sparked a revolution in biological imaging, allowing 'super-resolution' fluorescence microscopy below the diffraction limit of light. The last decade has seen an explosion in not only optical hardware for SMLM but also the development or repurposing of fluorescent proteins and small-molecule fluorescent probes for this technique. In this review, written by chemists for chemists, we detail the history of single-molecule localization microscopy and collate the collection of probes with demonstrated utility in SMLM. We hope it will serve as a primer for probe choice in localization microscopy as well as an inspiration for the development of new fluorophores that enable imaging of biological samples with exquisite detail.

Cells require a constant supply of fatty acids to survive and proliferate. Fatty acids incorporate into membrane and storage glycerolipids through a series of endoplasmic reticulum (ER) enzymes, but how these enzymes are regulated is not well understood. Here, using a combination of CRISPR-based genetic screens and unbiased lipidomics, we identified calcineurin B homologous protein 1 (CHP1) as a major regulator of ER glycerolipid synthesis. Loss of CHP1 severely reduces fatty acid incorporation and storage in mammalian cells and invertebrates. Mechanistically, CHP1 binds and activates GPAT4, which catalyzes the initial rate-limiting step in glycerolipid synthesis. GPAT4 activity requires CHP1 to be N-myristoylated, forming a key molecular interface between the two proteins. Interestingly, upon CHP1 loss, the peroxisomal enzyme, GNPAT, partially compensates for the loss of ER lipid synthesis, enabling cell proliferation. Thus, our work identifies a conserved regulator of glycerolipid metabolism and reveals plasticity in lipid synthesis of proliferating cells.

The emergence of new and increasingly sophisticated behaviors after birth is accompanied by dramatic increase of newly established synaptic connections in the nervous system. Little is known, however, of how nascent connections are organized to support such new behaviors alongside existing ones. To understand this, in the larval zebrafish we examined the development of spinal pathways from hindbrain V2a neurons and the role of these pathways in the development of locomotion. We found that new projections are continually layered laterally to existing neuropil, and give rise to distinct pathways that function in parallel to existing pathways. Across these chronologically layered pathways, the connectivity patterns and biophysical properties vary systematically to support a behavioral repertoire with a wide range of kinematics and dynamics. Such layering of new parallel circuits equipped with systematically changing properties may be central to the postnatal diversification and increasing sophistication of an animal's behavioral repertoire.

We present CLADES (Cell Lineage Access Driven by an Edition Sequence), a technology for cell lineage studies based on CRISPR/Cas9. CLADES relies on a system of genetic switches to activate and inactivate reporter genes in a pre-determined order. Targeting CLADES to progenitor cells allows the progeny to inherit a sequential cascade of reporters, coupling birth order with reporter expression. This gives us temporal resolution of lineage development that can be used to deconstruct an extended cell lineage by tracking the reporters expressed in the progeny. When targeted to the germ line, the same cascade progresses across animal generations, marking each generation with the corresponding combination of reporters. CLADES thus offers an innovative strategy for making programmable cascades of genes that can be used for genetic manipulation or to record serial biological events.

Serial-section electron microscopy such as FIB-SEM (focused ion beam scanning electron microscopy) has become an important tool for neuroscientists to trace the trajectories and global architecture of neural circuits in the brain, as well as to visualize the 3D ultrastructure of cellular organelles in neurons. In this study, we examined 3D features of mitochondria in electron microscope images generated from serial sections of four regions of mouse brains: nucleus accumbens (NA), hippocampal CA1, somatosensory cortex and dorsal cochlear nucleus (DCN). We compared mitochondria in the presynaptic terminals to those in the postsynaptic/dendritic compartments, and we focused on the shape and size of mitochondria. A common feature of mitochondria among the four brain regions is that presynaptic mitochondria generally are small and short, and most of them do not extend beyond presynaptic terminals. In contrast, the majority of postsynaptic/dendritic mitochondria are large and many of them spread through significant portions of the dendrites. Comparing among the brain areas, the cerebral cortex and DCN have even larger postsynaptic/dendritic mitochondria than the NA and CA1. Our analysis reveals that mitochondria in neurons are differentially sized and arranged according to their subcellular locations, suggesting a spatial organizing principle of mitochondria at the synapse.

Understanding the circuit mechanisms behind motion detection is a long-standing question in visual neuroscience. In , recent synapse-level connectomes in the optic lobe, particularly in ON-pathway (T4) receptive-field circuits, in concert with physiological studies, suggest an increasingly intricate motion model compared with the ubiquitous Hassenstein-Reichardt model, while our knowledge of OFF-pathway (T5) has been incomplete. Here we present a conclusive and comprehensive connectome that for the first time integrates detailed connectivity information for inputs to both T4 and T5 pathways in a single EM dataset covering the entire optic lobe. With novel reconstruction methods using automated synapse prediction suited to such a large connectome, we successfully corroborate previous findings in the T4 pathway and comprehensively identify inputs and receptive fields for T5. While the two pathways are likely evolutionarily linked and indeed exhibit many similarities, we uncover interesting differences and interactions that may underlie their distinct functional properties.

Imaging large samples at the resolution offered by electron microscopy is typically achieved by sequentially recording overlapping tiles that are later combined to seamless mosaics. Mosaics of serial sections are aligned to reconstruct three-dimensional volumes. To achieve this, image distortions and artifacts as introduced during sample preparation or imaging need to be removed.

In this chapter, we will discuss typical sources of artifacts and distortion, and we will learn how to use the open source software TrakEM2 to correct them.

The brain is worthy of study because it is in charge of behavior. A flurry of recent technical advances in measuring and quantifying naturalistic behaviors provide an important opportunity for advancing brain science. However, the problem of understanding unrestrained behavior in the context of neural recordings and manipulations remains unsolved, and developing approaches to addressing this challenge is critical. Here we discuss considerations in computational neuroethology-the science of quantifying naturalistic behaviors for understanding the brain-and propose strategies to evaluate progress. We point to open questions that require resolution and call upon the broader systems neuroscience community to further develop and leverage measures of naturalistic, unrestrained behavior, which will enable us to more effectively probe the richness and complexity of the brain.

Electrophysiology has long been the workhorse of neuroscience, allowing scientists to record with millisecond precision the action potentials generated by neurons in vivo. Recently, calcium imaging of fluorescent indicators has emerged as a powerful alternative. This technique has its own strengths and weaknesses and unique data processing problems and interpretation confounds. Here we review the computational methods that convert raw calcium movies to estimates of single neuron spike times with minimal human supervision. By computationally addressing the weaknesses of calcium imaging, these methods hold the promise of significantly improving data quality. We also introduce a new metric to evaluate the output of these processing pipelines, which is based on the cluster isolation distance routinely used in electrophysiology.

Numerous efforts to generate "connectomes," or synaptic wiring diagrams, of large neural circuits or entire nervous systems are currently underway. These efforts promise an abundance of data to guide theoretical models of neural computation and test their predictions. However, there is not yet a standard set of tools for incorporating the connectivity constraints that these datasets provide into the models typically studied in theoretical neuroscience. This article surveys recent approaches to building models with constrained wiring diagrams and the insights they have provided. It also describes challenges and the need for new techniques to scale these approaches to ever more complex datasets.