Bitbow Enables Highly Efficient Neuronal Lineage Tracing and Morphology Reconstruction in Single Drosophila Brains

Input density tunes Kenyon cell sensory responses in the Drosophila mushroom body

The ability to discriminate sensory stimuli with overlapping features is thought to arise in brain structures called expansion layers, where neurons carrying information about sensory features make combinatorial connections onto a much larger set of cells. For 50 years, expansion coding has been a prime topic of theoretical neuroscience, which seeks to explain how quantitative parameters of the expansion circuit influence sensory sensitivity, discrimination, and generalization. Here, we investigate the developmental events that produce the quantitative parameters of the arthropod expansion layer, called the mushroom body. Using Drosophila melanogaster as a model, we employ genetic and chemical tools to engineer changes to circuit development. These allow us to produce living animals with hypothesis-driven variations on natural expansion layer wiring parameters. We then test the functional and behavioral consequences. By altering the number of expansion layer neurons (Kenyon cells) and their dendritic complexity, we find that input density, but not cell number, tunes neuronal odor selectivity. Simple odor discrimination behavior is maintained when the Kenyon cell number is reduced and augmented by Kenyon cell number expansion. Animals with increased input density to each Kenyon cell show increased overlap in Kenyon cell odor responses and become worse at odor discrimination tasks.

Hacking brain development to test models of sensory coding

Animals can discriminate myriad sensory stimuli but can also generalize from learned experience. You can probably distinguish the favorite teas of your colleagues while still recognizing that all tea pales in comparison to coffee. Tradeoffs between detection, discrimination, and generalization are inherent at every layer of sensory processing. During development, specific quantitative parameters are wired into perceptual circuits and set the playing field on which plasticity mechanisms play out. A primary goal of systems neuroscience is to understand how material properties of a circuit define the logical operations-computations--that it makes, and what good these computations are for survival. A cardinal method in biology-and the mechanism of evolution--is to change a unit or variable within a system and ask how this affects organismal function. Here, we make use of our knowledge of developmental wiring mechanisms to modify hard-wired circuit parameters in the Drosophila melanogaster mushroom body and assess the functional and behavioral consequences. By altering the number of expansion layer neurons (Kenyon cells) and their dendritic complexity, we find that input number, but not cell number, tunes odor selectivity. Simple odor discrimination performance is maintained when Kenyon cell number is reduced and augmented by Kenyon cell expansion.

A Comparison of Lossless Compression Methods in Microscopy Data Storage Applications

Modern high-throughput microscopy methods such as light-sheet imaging and electron microscopy are capable of producing petabytes of data inside of a single experiment. Storage of these large images, however, is challenging because of the difficulty of moving, storing, and analyzing such vast amounts of data, which is often collected at very high data rates (>1GBps). In this report, we provide a comparison of the performance of several compression algorithms using a collection of published and unpublished datasets including confocal, fMOST, and pathology images. We also use simulated data to demonstrate the efficiency of each algorithm as image content or entropy increases. As a result of this work, we recommend the use of the BLOSC algorithm combined with ZSTD for various microscopy applications, as it produces the best compression ratio over a collection of conditions.

Towards a Comprehensive Optical Connectome at Single Synapse Resolution via Expansion Microscopy

Mapping and determining the molecular identity of individual synapses is a crucial step towards the comprehensive reconstruction of neuronal circuits. Throughout the history of neuroscience, microscopy has been a key technology for mapping brain circuits. However, subdiffraction size and high density of synapses in brain tissue make this process extremely challenging. Electron microscopy (EM), with its nanoscale resolution, offers one approach to this challenge yet comes with many practical limitations, and to date has only been used in very small samples such as C. elegans, tadpole larvae, fruit fly brain, or very small pieces of mammalian brain tissue. Moreover, EM datasets require tedious data tracing. Light microscopy in combination with tissue expansion via physical magnification-known as expansion microscopy (ExM)-offers an alternative approach to this problem. ExM enables nanoscale imaging of large biological samples, which in combination with multicolor neuronal and synaptic labeling offers the unprecedented capability to trace and map entire neuronal circuits in fully automated mode. Recent advances in new methods for synaptic staining as well as new types of optical molecular probes with superior stability, specificity, and brightness provide new modalities for studying brain circuits. Here we review advanced methods and molecular probes for fluorescence staining of the synapses in the brain that are compatible with currently available expansion microscopy techniques. In particular, we will describe genetically encoded probes for synaptic labeling in mice, zebrafish, Drosophila fruit flies, and C. elegans, which enable the visualization of post-synaptic scaffolds and receptors, presynaptic terminals and vesicles, and even a snapshot of the synaptic activity itself. We will address current methods for applying these probes in ExM experiments, as well as appropriate vectors for the delivery of these molecular constructs. In addition, we offer experimental considerations and limitations for using each of these tools as well as our perspective on emerging tools.