A significant challenge for developmental systems biology is balancing throughput with controlled conditions that minimize experimental artifacts. Large-scale developmental screens such as unbiased mutagenesis surveys have been limited in their applicability to embryonic systems, as the technologies for quantifying precise expression patterns in whole animals has not kept pace with other sequencing-based technologies. Here, we outline an open-source semi-automated pipeline to chemically fixate, stain, and 3D-image Drosophila embryos. Central to this pipeline is a liquid handling robot, Flyspresso, which automates the steps of classical embryo fixation and staining. We provide the schematics and an overview of the technology for an engineer or someone equivalently trained to reproduce and further improve upon Flyspresso, and highlight the Drosophila embryo fixation and colorimetric or antibody staining protocols. Additionally, we provide a detailed overview and stepwise protocol for our adaptive-feedback pipeline for automated embryo imaging on confocal microscopes. We demonstrate the efficiency of this pipeline compared to classical techniques, and how it can be repurposed or scaled to other protocols and biological systems. We hope our pipeline will serve as a platform for future research, allowing a broader community of users to build, execute, and share similar experiments.

Modern high-throughput bioimaging techniques pose the unprecedented challenge of exploring and analyzing the produced Terabyte-scale volumetric images directly in their 3D space. Without expensive virtual reality devices and/or parallel computing infrastructures, this becomes even more demanding and calls for new, more scalable tools that help exploring these very large 3D data also on common laptops and graphic hardware. To this end, we developed a plugin for the open-source, cross-platform Vaa3D system to extend its powerful 3D visualization and analysis capabilities to images of potentially unlimited size. When used with large volumetric images up to 2.5 Terabyte in size, Vaa3D-TeraFly exhibited real-time (subsecond) performance that scaled constantly on image size. The tool has been implemented in C++ with Qt and OpenGL and it is freely and publicly available both as open-source and as binary package along with the main Vaa3D distribution.

We describe an intensity-based glutamate-sensing fluorescent reporter (iGluSnFR) with signal-to-noise ratio and kinetics appropriate for in vivo imaging. We engineered iGluSnFR in vitro to maximize its fluorescence change, and we validated its utility for visualizing glutamate release by neurons and astrocytes in increasingly intact neurological systems. In hippocampal culture, iGluSnFR detected single field stimulus-evoked glutamate release events. In pyramidal neurons in acute brain slices, glutamate uncaging at single spines showed that iGluSnFR responds robustly and specifically to glutamate in situ, and responses correlate with voltage changes. In mouse retina, iGluSnFR-expressing neurons showed intact light-evoked excitatory currents, and the sensor revealed tonic glutamate signaling in response to light stimuli. In worms, glutamate signals preceded and predicted postsynaptic calcium transients. In zebrafish, iGluSnFR revealed spatial organization of direction-selective synaptic activity in the optic tectum. Finally, in mouse forelimb motor cortex, iGluSnFR expression in layer V pyramidal neurons revealed task-dependent single-spine activity during running.

Animals are not simple input-output machines. Their responses to even very similar stimuli are variable. A key, long-standing question in neuroscience is to understand the neural correlates of such behavioral variability. To reveal these correlates, behavior and neural population activity must be related to one another on single trials. Such analysis is challenging due to the dynamical nature of brain function (e.g., in decision making), heterogeneity across neurons and limited sampling of the relevant neural population. By analyzing population recordings from mouse frontal cortex in perceptual decision-making tasks, we show that an analysis approach tailored to the coarse grain features of the dynamics is able to reveal previously unrecognized structure in the organization of population activity. This structure is similar on error and correct trials, suggesting dynamics that may be constrained by the underlying circuitry, is able to predict multiple aspects of behavioral variability and reveals long time-scale modulation of population activity.

The fruit fly Drosophila melanogaster is an important model organism for neuroscience with a wide array of genetic tools that enable the mapping of individuals neurons and neural subtypes. Brain templates are essential for comparative biological studies because they enable analyzing many individuals in a common reference space. Several central brain templates exist for Drosophila, but every one is either biased, uses sub-optimal tissue preparation, is imaged at low resolution, or does not account for artifacts. No publicly available Drosophila ventral nerve cord template currently exists. In this work, we created high-resolution templates of the Drosophila brain and ventral nerve cord using the best-available technologies for imaging, artifact correction, stitching, and template construction using groupwise registration. We evaluated our central brain template against the four most competitive, publicly available brain templates and demonstrate that ours enables more accurate registration with fewer local deformations in shorter time.

The fruit fly Drosophila melanogaster is an important model organism for neuroscience with a wide array of genetic tools that enable the mapping of individual neurons and neural subtypes. Brain templates are essential for comparative biological studies because they enable analyzing many individuals in a common reference space. Several central brain templates exist for Drosophila, but every one is either biased, uses sub-optimal tissue preparation, is imaged at low resolution, or does not account for artifacts. No publicly available Drosophila ventral nerve cord template currently exists. In this work, we created high-resolution templates of the Drosophila brain and ventral nerve cord using the best-available technologies for imaging, artifact correction, stitching, and template construction using groupwise registration. We evaluated our central brain template against the four most competitive, publicly available brain templates and demonstrate that ours enables more accurate registration with fewer local deformations in shorter time.

Behaviors involving the interaction of multiple individuals are complex and frequently crucial for an animal's survival. These interactions, ranging across sensory modalities, length scales, and time scales, are often subtle and difficult to characterize. Contextual effects on the frequency of behaviors become even more difficult to quantify when physical interaction between animals interferes with conventional data analysis, e.g. due to visual occlusion. We introduce a method for quantifying behavior in fruit fly interaction that combines high-throughput video acquisition and tracking of individuals with recent unsupervised methods for capturing an animal's entire behavioral repertoire. We find behavioral differences between solitary flies and those paired with an individual of the opposite sex, identifying specific behaviors that are affected by social and spatial context. Our pipeline allows for a comprehensive description of the interaction between two individuals using unsupervised machine learning methods, and will be used to answer questions about the depth of complexity and variance in fruit fly courtship.

Descending neurons (DNs) occupy a key position in the sensorimotor hierarchy, conveying signals from the brain to the rest of the body below the neck. In Drosophila melanogaster flies, approximately 480 DN cell types have been described from electron-microscopy image datasets. Genetic access to these cell types is crucial for further investigation of their role in generating behaviour. We previously conducted the first large-scale survey of Drosophila melanogaster DNs, describing 98 unique cell types from light microscopy and generating cell-type-specific split-Gal4 driver lines for 65 of them. Here, we extend our previous work, describing the morphology of 137 additional DN types from light microscopy, bringing the total number DN types identified in light microscopy datasets to 235, or nearly 50%. In addition, we produced 500 new sparse split-Gal4 driver lines and compiled a list of previously published DN lines from the literature for a combined list of 738 split-Gal4 driver lines targeting 171 DN types.

Simultaneous recordings from hundreds or thousands of neurons are becoming routine because of innovations in instrumentation, molecular tools, and data processing software. Such recordings can be analyzed with data science methods, but it is not immediately clear what methods to use or how to adapt them for neuroscience applications. We review, categorize, and illustrate diverse analysis methods for neural population recordings and describe how these methods have been used to make progress on longstanding questions in neuroscience. We review a variety of approaches, ranging from the mathematically simple to the complex, from exploratory to hypothesis-driven, and from recently developed to more established methods. We also illustrate some of the common statistical pitfalls in analyzing large-scale neural data.

The C. elegans cell lineage provides a unique opportunity to look at how cell lineage affects patterns of gene expression. We developed an automatic cell lineage analyzer that converts high-resolution images of worms into a data table showing fluorescence expression with single-cell resolution. We generated expression profiles of 93 genes in 363 specific cells from L1 stage larvae and found that cells with identical fates can be formed by different gene regulatory pathways. Molecular signatures identified repeating cell fate modules within the cell lineage and enabled the generation of a molecular differentiation map that reveals points in the cell lineage when developmental fates of daughter cells begin to diverge. These results demonstrate insights that become possible using computational approaches to analyze quantitative expression from many genes in parallel using a digital gene expression atlas.