Biology subjects

Chan, K.-S.

Publications and source records attributed to Chan, K.-S..

5 recordsLinked to original sources

bioRxiv2021

Metabolic Derangement in Polycystic Kidney Disease Mouse Models Is Ameliorated by Mitochondrial-Targeted Antioxidants

Autosomal dominant polycystic kidney disease (ADPKD) is characterized by progressively enlarging cysts. Here we elucidate the interplay between oxidative stress, mitochondrial dysfunction, and metabolic derangement using two mouse models of PKD1 mutation, PKD1RC/null and PKD1RC/RC. Mouse kidneys with PKD1 mutation have decreased mitochondrial complexes activity. Targeted proteomics analysis shows a significant decrease in proteins involved in the TCA cycle, fatty acid oxidation (FAO), respiratory complexes, and endogenous antioxidants. Overexpressing mitochondrial-targeted catalase (mCAT) using adeno-associated virus reduces mitochondrial ROS, oxidative damage, ameliorates the progression of PKD and partially restores expression of proteins involved in FAO and the TCA cycle. In human ADPKD cells, inducing mitochondrial ROS increased ERK1/2 phosphorylation and decreased AMPK phosphorylation, whereas the converse was observed with increased scavenging of ROS in the mitochondria. Treatment with the mitochondrial protective peptide, SS31, recapitulates the beneficial effects of mCAT, supporting its potential application as a novel therapeutic for ADPKD.

pathology↗

bioRxiv2021

Studying magnetic susceptibility, microstructural compartmentalisation and chemical exchange in a formalin-fixed ex vivo human brain specimen

PurposeEx vivo imaging is a preferable method to study the biophysical mechanism of white matter orientation-dependent signal phase evolution. Yet, how formalin fixation, commonly used for tissue preservation, affects the phase measurement is not fully known. We, therefore, study the impacts of formalin fixation on magnetic susceptibility, microstructural compartmentalisation and chemical exchange measurement on human brain tissue. MethodsA formalin-fixed, post-mortem human brain specimen was scanned with multiple orientations with respect to the main magnetic field direction for robust bulk magnetic susceptibility measurement with conventional quantitative susceptibility imaging models. Homogeneous white matter tissues were subsequently excised from the whole-brain specimen and scanned in multiple rotations on an MRI scanner to measure the anisotropic magnetic susceptibility and microstructure-related contributions in the signal phase. Electron microscopy was used to validate the MRI findings. ResultsThe bulk isotropic magnetic susceptibility of ex vivo whole-brain imaging is comparable to in vivo imaging, with noticeable enhanced non-susceptibility contributions. The excised specimen experiment reveals that anisotropic magnetic susceptibility and compartmentalisation phase effect were considerably reduced in formalin-fixed white matter tissue. ConclusionsDespite formalin-fixed white matter tissue has comparable bulk isotropic magnetic susceptibility to those measured via in vivo imaging, its orientation-dependent components in the signal phase related to the tissue microstructure is substantially weaker, making it less favourable in white matter microstructure studies using phase imaging.

biophysics↗

bioRxiv2020

QSM Reconstruction Challenge 2.0 Part 1: A Realistic in silico Head Phantom for MRI data simulation and evaluation of susceptibility mapping procedures

PurposeTo create a realistic in-silico head phantom for the second Quantitative Susceptibility Mapping (QSM) Reconstruction Challenge and for future evaluations of processing algorithms for QSM. MethodsWe created a digital whole-head tissue property phantom by segmenting and post-processing high-resolution (0.64mm isotropic), multi-parametric MRI data acquired at 7T from a healthy volunteer. We simulated the steady-state magnetization at 7T using a Bloch simulator and mimicked a Cartesian sampling scheme through Fourier-based processing. Computer code for generating the phantom and performing the MR simulation was designed to facilitate flexible modifications of the phantom in the future, such as the inclusion of pathologies, as well as the simulation of a wide range of acquisition protocols. Specifically, the following parameters and effects were implemented: repetition time and echo time, voxel size, background fields, and RF phase biases. Diffusion weighted imaging phantom data is provided allowing future investigations of tissue microstructure effects in phase and QSM algorithms. ResultsThe brain-part of the phantom featured realistic morphology with spatial variations in relaxation and susceptibility values similar to the in vivo setting. We demonstrated some of the phantoms properties, including the possibility of generating phase data with non-linear evolution over echo time due to partial volume effects or complex distributions of frequency shifts within the voxel. ConclusionThe presented phantom and computer programs are publicly available and may serve as a ground truth in future assessments of the faithfulness of quantitative susceptibility reconstruction algorithms.

neuroscience↗

bioRxiv2020

SEPIA - SuscEptibility mapping PIpeline tool for phAse images

Quantitative susceptibility mapping (QSM) is a physics-driven computational technique that has a high sensitivity in quantifying iron deposition based on MRI phase images. Furthermore, it has a unique ability to distinguish paramagnetic and diamagnetic contributions such as haemorrhage and calcification based on image contrast. These properties have contributed to a growing interest to use QSM not only in research but also in clinical applications. However, it is challenging to obtain high quality susceptibility map because of its ill-posed nature, especially for researchers who have less experience with QSM and the optimisation of its pipeline. In this paper, we present an open-source processing pipeline tool called SuscEptibility mapping PIpeline tool for phAse images (SEPIA) dedicated to the post-processing of MRI phase images and QSM. SEPIA connects various QSM toolboxes freely available in the field to offer greater flexibility in QSM processing. It also provides an interactive graphical user interface to construct and execute a QSM processing pipeline, simplifying the workflow in QSM research. The extendable design of SEPIA also allows developers to deploy their methods in the framework, providing a platform for developers and researchers to share and utilise the state-of-the-art methods in QSM.

neuroscience↗

bioRxiv2020

White Matter microstructural property decoding from gradient echo data using realistic white matter models

Multi-echo gradient echo (ME-GRE) magnetic resonance signal evolution in white matter has a strong dependence on the orientation of myelinated axons with respect to the main static field. Although analytical solutions have been able to predict some of the white matter (WM) signal behaviour of the hollow cylinder model, it has been shown that realistic models of WM offer a better description of the signal behaviour observed. In this work, we present a pipeline to (i) generate realistic 2D WM models with their microstructure based on real axon morphology with adjustable fiber volume fraction (FVF) and g-ratio. We (ii) simulate their interaction with the static magnetic field to be able to simulate their MR signal. For the first time, we (iii) demonstrate that realistic 2D WM models can be used to simulate a MR signal that provides a good approximation of the signal obtained from a real 3D WM model derived from electron microscopy. We then (iv) demonstrate in silico that 2D WM models can be used to predict microstructural parameters in a robust way if ME-GRE multi-orientation data is available and the main fiber orientation in each pixel is known using DTI. A deep learning network was trained and characterized in its ability to recover the desired microstructural parameters such as FVF, g-ratio, free and bound water transverse relaxation and magnetic susceptibility. Finally, the network was trained to recover these micro-structural parameters from an ex vivo dataset acquired in 9 orientations with respect to the magnetic field and 12 echo times. We demonstrate that this is an overdetermined problem and that as few as 3 orientations can already provide comparable results for some of the decoded metrics. [Highlights] - A pipeline to generate realistic white models of arbitrary fiber volume fraction and g-ratio is presented; - We present a methodology to simulated the gradient echo signal from segmented 2D and 3D models of white matter, which takes into account the interaction of the static magnetic field with the anisotropic susceptibility of the myelin phospholipids; - Deep Learning Networks can be used to decode microstructural white matter parameters from the signal of multi-echo multi-orientation data;

neuroscience↗