in-vivo-whole-brain-imaging-zebrafish-larvae-using-three-dimensional

<em>In Vivo</em> Whole-Brain Imaging of Zebrafish Larvae Using Three-Dimensional Fluorescence Microscopy

As a vertebrate model animal, larval zebrafish are widely used in neuroscience and provide a unique opportunity to monitor whole-brain activity at the ...

KAIST

KAIST Institute for Health Science and Technology

In this paper, an entropy based method for quantifying the depth of anesthesia from rat EEG is presented. The proposed index for the depth of anesthesia called modified Shannon entropy (MShEn) is based on Shannon entropy (ShEn) and spectral entropy (SpEn) which are widely used for analyzing non-stationary signals. Discrimination power (DP), as a performance indicator for indexes, is defined and used to derive the final index for the depth of anesthesia. For experiment, EEG from anesthetized rats are measured and analyzed by using MShEn. MShEn shows both high stability and high correlation with other indexes for depth of anesthesia.

Monitoring the depth of anesthesia from rat EEG using modified Shannon entropy analysis.

Methods for one-photon fluorescent imaging of calcium dynamics can capture the activity of hundreds of neurons across large fields of view at a low equipment complexity and cost. In contrast to two-photon methods, however, one-photon methods suffer from higher levels of crosstalk from neuropil, resulting in a decreased signal-to-noise ratio and artifactual correlations of neural activity. We address this problem by engineering cell-body-targeted variants of the fluorescent calcium indicators GCaMP6f and GCaMP7f. We screened fusions of GCaMP to natural, as well as artificial, peptides and identified fusions that localized GCaMP to within 50 μm of the cell body of neurons in mice and larval zebrafish. One-photon imaging of soma-targeted GCaMP in dense neural circuits reported fewer artifactual spikes from neuropil, an increased signal-to-noise ratio, and decreased artifactual correlation across neurons. Thus, soma-targeting of fluorescent calcium indicators facilitates usage of simple, powerful, one-photon methods for imaging neural calcium dynamics.

Precision Calcium Imaging of Dense Neural Populations via a Cell-Body-Targeted Calcium Indicator.

Ultra-multiplexed fluorescence imaging requires the use of spectrally overlapping fluorophores to label proteins and then to unmix the images of the fluorophores. However, doing this remains a challenge, especially in highly heterogeneous specimens, such as the brain, owing to the high degree of variation in the emission spectra of fluorophores in such specimens. Here, we propose PICASSO, which enables more than 15-color imaging of spatially overlapping proteins in a single imaging round without using any reference emission spectra. PICASSO requires an equal number of images and fluorophores, which enables such advanced multiplexed imaging, even with bandpass filter-based microscopy. We show that PICASSO can be used to achieve strong multiplexing capability in diverse applications. By combining PICASSO with cyclic immunofluorescence staining, we achieve 45-color imaging of the mouse brain in three cycles. PICASSO provides a tool for multiplexed imaging with high accessibility and accuracy for a broad range of researchers.

PICASSO allows ultra-multiplexed fluorescence imaging of spatially overlapping proteins without reference spectra measurements.

Three-dimensional fluorescence microscopy has an intrinsic performance limit set by the number of photons that can be collected from the sample in a given time interval. Here, we extend our earlier work - a recursive light propagation network (RLP-Net) - which is a computational microscopy technique that overcomes such limitations through virtual refocusing that enables volume reconstruction from two adjacent 2-D wide-field fluorescence images. RLP-Net employs a recursive inference scheme in which the network progressively predicts the subsequent planes along the axial direction. This recursive inference scheme reflects that the law of physics for the light propagation remains spatially invariant and therefore a fixed function (i.e., a neural network) for a short distance light propagation can be recursively applied for a longer distance light propagation. In addition, we employ a self-supervised denoising method to enable accurate virtual light propagation over a long distance. We demonstrate the capability of our method through high-speed volumetric imaging of neuronal activity of a live zebrafish brain. The source code used in the paper is available at https://github.com/NICALab/rlpnet.

Young-Gyu Yoon

School of Electrical Engineering

KAIST

In Vivo Whole-Brain Imaging of Zebrafish Larvae Using Three-Dimensional Fluorescence Microscopy