The vast majority of the adult fly ventral nerve cord is composed of 34 hemilineages, which are clusters of lineally related neurons. Neurons in these hemilineages use one of the three fast-acting neurotransmitters (acetylcholine, GABA, or glutamate) for communication. We generated a comprehensive neurotransmitter usage map for the entire ventral nerve cord. We did not find any cases of neurons using more than one neurotransmitter, but found that the acetylcholine specific gene ChAT is transcribed in many glutamatergic and GABAergic neurons, but these transcripts typically do not leave the nucleus and are not translated. Importantly, our work uncovered a simple rule: All neurons within a hemilineage use the same neurotransmitter. Thus, neurotransmitter identity is acquired at the stem cell level. Our detailed transmitter- usage/lineage identity map will be a great resource for studying the developmental basis of behavior and deciphering how neuronal circuits function to regulate behavior.

Mbd3, a member of nucleosome remodeling and deacetylase (NuRD) co-repressor complex, was previously identified as an inhibitor for deterministic induced pluripotent stem cell (iPSC) reprogramming, where up to 100% of donor cells successfully complete the process. NuRD can assume multiple mutually exclusive conformations, and it remains unclear whether this deterministic phenotype can be attributed to a specific Mbd3/NuRD subcomplex. Moreover, since complete ablation of Mbd3 blocks somatic cell proliferation, we aimed to explore functionally relevant alternative ways to neutralize Mbd3-dependent NuRD activity. We identify Gatad2a, a NuRD-specific subunit, whose complete deletion specifically disrupts Mbd3/NuRD repressive activity on the pluripotency circuitry during iPSC differentiation and reprogramming without ablating somatic cell proliferation. Inhibition of Gatad2a facilitates deterministic murine iPSC reprogramming within 8 days. We validate a distinct molecular axis, Gatad2a-Chd4-Mbd3, within Mbd3/NuRD as being critical for blocking reestablishment of naive pluripotency and further highlight signaling-dependent and post-translational modifications of Mbd3/NuRD that influence its interactions and assembly.

Reconstructing a connectome from an EM dataset often requires a large effort of proofreading automatically generated segmentations. While many tools exist to enable tracing or proofreading, recent advances in EM imaging and segmentation quality suggest new strategies and pose unique challenges for tool design to accelerate proofreading. Namely, we now have access to very large multi-TB EM datasets where (1) many segments are largely correct, (2) segments can be very large (several GigaVoxels), and where (3) several proofreaders and scientists are expected to collaborate simultaneously. In this paper, we introduce NeuTu as a solution to efficiently proofread large, high-quality segmentation in a collaborative setting. NeuTu is a client program of our high-performance, scalable image database called DVID so that it can easily be scaled up. Besides common features of typical proofreading software, NeuTu tames unprecedentedly large data with its distinguishing functions, including: (1) low-latency 3D visualization of large mutable segmentations; (2) interactive splitting of very large false merges with highly optimized semi-automatic segmentation; (3) intuitive user operations for investigating or marking interesting points in 3D visualization; (4) visualizing proofreading history of a segmentation; and (5) real-time collaborative proofreading with lock-based concurrency control. These unique features have allowed us to manage the workflow of proofreading a large dataset smoothly without dividing them into subsets as in other segmentation-based tools. Most importantly, NeuTu has enabled some of the largest connectome reconstructions as well as interesting discoveries in the fly brain.

Real-time lineage tracing in flies gets a boost with three techniques to specifically label a progenitor’s daughter cells.

How memories of past events influence behavior is a key question in neuroscience. The major associative learning center in Drosophila, the Mushroom Body (MB), communicates to the rest of the brain through Mushroom Body Output Neurons (MBONs). While 21 MBON cell types have their dendrites confined to small compartments of the MB lobes, analysis of EM connectomes revealed the presence of an additional 14 MBON cell types that are atypical in having dendritic input both within the MB lobes and in adjacent brain regions. Genetic reagents for manipulating atypical MBONs and experimental data on their functions has been lacking. In this report we describe new cell-type-specific GAL4 drivers for many MBONs, including the majority of atypical MBONs. Using these genetic reagents, we conducted optogenetic activation screening to examine their ability to drive behaviors and learning. These reagents provide important new tools for the study of complex behaviors in Drosophila.

Temporal focusing (TF) multiphoton systems constitute a powerful solution for cellular resolution optogenetic stimulation and recording in three-dimensional, scattering tissue. Here, we address two fundamental aspects in the design of such systems: first, we examine the design of TF systems with specific optical sectioning by comparatively analyzing previously published results. Next, we develop a solution for obtaining TF in a flexible three-dimensional pattern of cellmatched focal spots. Our solution employs spatio-temporal focusing (SSTF) in a unique optical system design that can be integrated before essentially any multiphoton imaging or stimulation system.

Chemical signaling between neurons in the brain can be divided into two major categories: fast synaptic transmission and neuromodulation. Fast synaptic transmission, mediated by amino acids such as glutamate and GABA, occurs on millisecond time scales and results in the influx of ions through ligand-gated ion channels on postsynaptic neurons (Figure 1A). Electrophysiological and optical imaging tools, including genetically encoded voltage indicators, have enabled neuroscientists to link cause (neurotransmitter release) and effect (membrane polarization) of synaptic transmission in time and space. Unlike classical neurotransmitters, neuromodulators do not produce immediate electrical effects that excite or inhibit target neurons. Instead, neuromodulators tune the intrinsic or synaptic properties of neurons, most commonly through interaction with G-protein-coupled receptors (GPCRs) (Figure 1B). Neuromodulators can escape the synaptic cleft and diffuse broadly, allowing them to influence the activity of many neurons in a state-dependent manner. Therefore, the spatial component of neuromodulator flux is fundamentally important. However, the temporal and/or spatial limitations of techniques classically used to study neuromodulation, such as microdialysis and fast-scan cyclic voltammetry (FSCV), make it difficult to interpret how neuromodulator release affects the plasticity or function of target neuronal populations on a moment-to-moment basis. Therefore, tools that can detect neuromodulators with high spatiotemporal resolution are critical for understanding their impact on neural computations that control behavior in health and disease.

Visualization of cellular and molecular processes is an indispensable tool for cell biologists, and innovations in microscopy methods unfailingly lead to new biological discoveries. Today, light microscopy (LM) provides ever-higher spatial and temporal resolution and visualization of biological process over enormous ranges. Electron microscopy (EM) is moving into the atomic resolution regime and allowing cellular analyses that are more physiological and sophisticated in scope. Importantly, much is being gained by combining multiple approaches, (e.g., LM and EM) to take advantage of their complementary strengths. The advent of high-throughput microscopies has led to a common need for sophisticated computational methods to quantitatively analyze huge amounts of data and translate images into new biological insights.

Because of its genetic, molecular, and behavioral tractability, Drosophila has emerged as a powerful model system for studying molecular and cellular mechanisms underlying the development and function of nervous systems. The Drosophila nervous system has fewer neurons and exhibits a lower glia:neuron ratio than is seen in vertebrate nervous systems. Despite the simplicity of the Drosophila nervous system, glial organization in flies is as sophisticated as it is in vertebrates. Furthermore, fly glial cells play vital roles in neural development and behavior. In addition, powerful genetic tools are continuously being created to explore cell function in vivo. In taking advantage of these features, the fly nervous system serves as an excellent model system to study general aspects of glial cell development and function in vivo. In this article, we review and discuss advanced genetic tools that are potentially useful for understanding glial cell biology in Drosophila.

Multilocus sequence typing (MLST) has become the preferred method for genotyping many biological species, and it is especially useful for analyzing haploid eukaryotes. MLST is rigorous, reproducible, and informative, and MLST genotyping has been shown to identify major phylogenetic clades, molecular groups, or subpopulations of a species, as well as individual strains or clones. MLST molecular types often correlate with important phenotypes. Conventional MLST involves the extraction of genomic DNA and the amplification by PCR of several conserved, unlinked gene sequences from a sample of isolates of the taxon under investigation. In some cases, as few as three loci are sufficient to yield definitive results. The amplicons are sequenced, aligned, and compared by phylogenetic methods to distinguish statistically significant differences among individuals and clades. Although MLST is simpler, faster, and less expensive than whole genome sequencing, it is more costly and time-consuming than less reliable genotyping methods (e.g. amplified fragment length polymorphisms). Here, we describe a new MLST method that uses next-generation sequencing, a multiplexing protocol, and appropriate analytical software to provide accurate, rapid, and economical MLST genotyping of 96 or more isolates in single assay. We demonstrate this methodology by genotyping isolates of the well-characterized, human pathogenic yeast Cryptococcus neoformans.