Dense neuronal reconstruction through X-ray holographic nano-tomography

Pacureanu, Alexandra; Maniates-Selvin, Jasper T.; Kuan, Aaron T.; Thomas, Logan A.; Chen, Chiao-Lin; Cloetens, Peter; Lee, Wei-Chung Allen

doi:10.1101/653188

Cited by 10 publications

(9 citation statements)

References 44 publications

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“…Presumably, Snodgrass named it a reductor muscle because depressor or levator would be inaccurate. We agree with the characterization of Miller and Soler that muscles 40 and 41 are two distinct muscles as they attach via distinct tendons, as seen in X-ray images of the leg musculature (Pacureanu et al, 2019). Because the function of muscle 41 is to flex the tibia, for clarity we refer to it here as a tibia flexor.…”

Section: Experimental Procedures Identifying Gal4 Lines That Label Lesupporting

confidence: 89%

A size principle for leg motor control inDrosophila

Azevedo

Dickinson

Gurung

et al. 2019

Preprint

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To move the body, the brain must precisely coordinate patterns of activity among diverse populations of motor neurons. In many species, including vertebrates, the motor neurons innervating a given muscle fire in a specific order that is determined by a gradient of cellular size and electrical excitability. This hierarchy allows premotor circuits to recruit motor neurons of increasing force capacity in a task-dependent manner. However, it remains unclear whether such a size principle also applies to species with more compact motor systems, such as the fruit fly, Drosophila melanogaster, which has just 53 motor neurons per leg. Using in vivo calcium imaging and electrophysiology, we found that genetically-identified motor neurons controlling flexion of the fly tibia exhibit a gradient of anatomical, physiological, and functional properties consistent with the size principle. Large, fast motor neurons control high force, ballistic movements while small, slow motor neurons control low force, postural movements. Intermediate neurons fall between these two extremes. In behaving flies, motor neurons are recruited in order from slow to fast. This hierarchical organization suggests that slow and fast motor neurons control distinct motor regimes. Indeed, we find that optogenetic manipulation of each motor neuron type has distinct effects on the behavior of walking flies.

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Section: Experimental Procedures Identifying Gal4 Lines That Label Lesupporting

confidence: 89%

A size principle for leg motor control inDrosophila

Azevedo

Dickinson

Gurung

et al. 2019

Preprint

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“…1A). The FeCO is the largest proprioceptive organ in the fruit fly, Drosophila melanogaster (Meigen, 1830), and its 152 neurons can be divided into at least three anatomically distinct subtypes: the claw, hook, and club neurons (Mamiya et al, 2018;Maniates-Selvin et al, 2020;Pacureanu et al, 2019;Phillis et al, 1996). Each subtype encodes different kinematic features of the femur-tibia joint: claw neurons encode tibia position (flexion or extension; Fig.…”

Section: Introductionmentioning

confidence: 99%

Central processing of leg proprioception inDrosophila

Agrawal

Dickinson

Sustar

et al. 2020

Preprint

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Proprioception, the sense of self-movement and position, is mediated by mechanosensory neurons that detect diverse features of body kinematics. Although proprioceptive feedback is crucial for accurate motor control, little is known about how downstream circuits transform limb sensory information to guide motor output. Here, we investigate neural circuits in Drosophila that process proprioceptive information from the fly leg. We identify three cell-types from distinct developmental lineages that are positioned to receive input from proprioceptor subtypes encoding tibia position, movement, and vibration. 13Bα neurons encode femur-tibia joint angle and mediate postural changes in tibia position. 9Aα neurons also drive changes in leg posture, but encode a combination of directional movement, high frequency vibration, and joint angle. Activating 10Bα neurons, which encode tibia vibration at specific joint angles, elicits pausing in walking flies. Altogether, our results reveal that central circuits integrate information across proprioceptor subtypes to construct complex sensorimotor representations that mediate diverse behaviors, including reflexive control of limb posture and detection of leg vibration.

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“…Several workflows have so far been established that solved the above-mentioned challenges on sample preparation, relocalization of ROIs, and data correlation. Recent examples for multiscale combinations of in-vivo and ex-vivo imaging include the correlation of intravital microscopy, CT and EM to study single tumor cells in the cerebral vasculature [81]; correlation of X-ray holographic nano-tomography, EM and FM to disentangle dense neuronal circuitry in Drosophila melanogaster and mammalian central and peripheral nervous tissue [82]; correlation of local neuronal and capillary responses by two-photon microscopy with mesoscopic responses detected by ultrasound (US) and BOLD-fMRI [83]; or extended CMI pipelines that include the correlation of a variety of imaging technologies, such as non-invasive US, CT and highresolution episcopic microscopy (HREM) for phenotyping left/right asymmetries of all visceral organs in a mouse model of heterotaxy or combined OCT, PAI and HREM of chick embryos at multiple development stages [8,84,85]. Further examples of novel CMI pipelines that uncover biophysical or chemical information include the correlation of FM, molecular (MALDI MSI) and elemental imaging [X-ray fluorescence (XRF)] to analyze lipids and elements relevant to bone structures in the very same sample section of a chicken phalanx without tissue decalcification at the µm scales [86].…”

Section: Novel CMI Pipelinesmentioning

confidence: 99%