Biology subjects

Moradi, N.

Publications and source records attributed to Moradi, N..

6 recordsLinked to original sources

bioRxiv2025

Isolation of functional lysosomes from skeletal muscle

Lysosomes are membrane-bound organelles responsible for the degradation of damaged or dysfunctional cellular components, including mitochondria. Their acidic internal environment and the presence of an array of hydrolytic enzymes facilitate the efficient breakdown of macromolecules such as proteins, lipids, and nucleic acids. Mitochondria play a critical role in maintaining skeletal muscle homeostasis to meet the energy demands under physiological and pathological conditions. Mitochondrial quality control within skeletal muscle during processes such as exercise, disuse, and injury is regulated by mitophagy, where dysfunctional mitochondria are targeted for lysosomal degradation. The limited understanding of quality control mechanisms in skeletal muscle necessitates the need for isolating intact lysosomes to assess organelle integrity and the degradative functions of hydrolytic enzymes. Although several methods exist for lysosome isolation, the complex structure of skeletal muscle makes it challenging to obtain relatively pure and functional lysosomes due to the high abundance of contractile proteins. Here we describe a method to isolate functional lysosomes from small amounts of mouse skeletal muscle tissue, preserving membrane integrity. We also describe functional assays that allow direct evaluation of lysosomal enzymatic activity and we provide data indicating reduced lysosomal degradative activity in lysosomes from aging muscle. We hope that this protocol provides a valuable tool to advance our understanding of lysosomal biology in skeletal muscle, supporting investigations into lysosome-related dysfunction in aging, disease, and exercise adaptations. New and NoteworthyLysosomes within skeletal muscle function to degrade dysfunctional debris and initiate retrograde signaling pathways. We developed a method to isolate purified lysosomal fractions using a small portion of skeletal muscle, eliminating the need for density gradients or lysosome-modifying agents, ensuring high lysosomal purity without compromising structure or function. By enabling functional analysis via acid phosphatase, cathepsin-B activity, and calcium release, this approach offers a powerful tool to study lysosomal roles in muscle physiology, disease, and exercise. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/675181v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@f41e09org.highwire.dtl.DTLVardef@e013a0org.highwire.dtl.DTLVardef@12871edorg.highwire.dtl.DTLVardef@4f96cb_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2023

Rethinking sinking: Imaging the flow fields of natural marine aggregates to derive sinking velocity

The marine biological carbon pump is mainly driven by the interplay between aggregate sinking velocity and remineralization. Sinking velocity of natural marine aggregates is not routinely measured but often calculated using Stokes law, which does not consider size-dependent changes in porosity. We analyzed the flow fields around 81 in situ-formed aggregates using Particle Image Velocimetry (PIV) to determine the factors controlling aggregate settling. Using an independently derived scaling of porosity with size, we predicted the sinking velocity of laboratory-formed and in situ-formed aggregates with known densities. Small aggregates (<500 {micro}m) have relatively lower porosities than large aggregates, and their increased compactness and density leads to higher size-specific settling velocities, and generally higher carbon-to-volume ratios. Applying our scaling approach to a global data set of vertical aggregate abundance and size distribution, we found that small aggregates contribute 40-70% to total carbon fluxes in situ. TeaserImproved sinking velocity prediction for marine aggregates highlights the contribution of small aggregates to carbon sequestration.

biophysics↗

bioRxiv2022

Deep EEG Source Localization via EMD-based fMRI High Spatial Frequency

Brain imaging with a high-spatiotemporal resolution is crucial for accurate brain-function mapping. Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are two popular neuroimaging modalities with complementary features that record brain function with high temporal and spatial resolution, respectively. One popular non-invasive way to obtain data with both high spatial and temporal resolutions is to combine the fMRI activation map and EEG data to improve the spatial resolution of the EEG source localization. However, using the whole fMRI map may cause spurious results for the EEG source localization, especially for deep brain regions. Considering the heads conductivity, deep regions sources with low activity are unlikely to be detected by the EEG electrodes at the scalp. In this study, we use fMRIs high spatial-frequency component to identify the local high-intensity activations that are most likely to be captured by the EEG. The 3D Empirical Mode Decomposition (3D-EMD), a data-driven method, is used to decompose the fMRI map into its spatial-frequency components. Different validation measurements for EEG source localization show improved performance for the EEG inverse-modeling informed by the fMRIs high-frequency spatial component compared to the fMRI-informed EEG source-localization methods. The level of improvement varies depending on the voxels intensity and their distribution. Our experimental results also support this conclusion.

neuroscience↗

bioRxiv2021

Holo-Hilbert Spectral-based Removal of Gamma-Band Noise from Simultaneous EEG-fMRI Recordings

Simultaneous EEG-fMRI is a growing and promising field, as it has great potential to further our understanding of the spatiotemporal dynamics of brain function in health and disease. In particular, there is much interest in understanding the fMRI correlates of brain activity in the gamma band (> 30 Hz), as these frequencies are thought to be associated with cognitive processes involving perception, attention, and memory, as well as with disorders such as schizophrenia and autism. However, progress in this area has been limited due to issues such as MR-induced artifacts in EEG recordings, which seem to be more problematic for gamma frequencies. This paper presents a noise removal method for the gamma band of EEG that is based on the Holo-Hilbert spectral analysis (HHSA), but with a new implementation strategy. HHSA uses a nested empirical mode decomposition (EMD) to identify amplitude and frequency modulations (AM and FM, respectively) by averaging over frequencies with high and significant powers. Our method examines gamma band by applying two layers of EMD to the FM and AM components, removing components with very low power based on the power-instantaneous frequency spectrum, and subsequently reconstructs the denoised gamma-band signal from the remaining components. Simulations demonstrate that our proposed method efficiently reduces artifacts while preserving the original gamma signal which is especially critical for simultaneous EEG/fMRI studies.

neuroscience↗

bioRxiv2019

Estimation of Global and Local Complexities of Brain Networks: A Random Walks Approach

The complexity of brain activity has been observed at many spatial scales and there exists increasing evidence supporting its use in differentiating between mental states and disorders. Here we proposed a new measure of network (global) complexity that is constructed as the sum of the complexities of its nodes (i.e, local complexity). The local complexity of each node is regarded as an index that compares the sample entropy of the time series generated by the movement of a random walker on the network resulting from removing the node and its connections, with the sample entropy of the time series obtained from a regular lattice (the ordered state) and an Erdos-Renyi network (disordered state). We studied the complexity of fMRI-based resting-state functional networks. We found that positively correlated, or \"pos\", network comprising only the positive functional connections has higher complexity than the anticorrelation (\"neg\") network (comprising the negative functional connections) and the network consisting of the absolute value of all connections (\"abs\"). We also found a significant correlation between complexity and the strength of functional connectivity. For the pos network this correlation is significantly weaker at the local scale compared to the global scale, whereas for the neg network the link is stronger at the local scale than at the global scale, but still weaker than for the pos network. Our results suggest that the pos network is related to the information processing in the brain and should be used for functional connectivity analysis instead of the abs network as is usually done.

neuroscience↗

bioRxiv2019

Spatiotemporal empirical mode decomposition of resting-state fMRI signals: application to global signal regression

Resting-state functional connectivity MRI (rs-fcMRI) is a common method for mapping functional brain networks. However, estimation of these networks is affected by the presence of a common global systemic noise, or global signal (GS). Previous studies have shown that the common preprocessing steps of removing the GS may create spurious correlations between brain regions. In this paper, we decompose fMRI signals into 5 spatial and 3 temporal intrinsic mode functions (SIMF and TIMF, respectively) by means of the empirical mode decomposition (EMD), which is an adaptive data-driven method widely used to analyze nonlinear and nonstationary phenomena. For each SIMF, brain connectivity matrices were computed by means of the Pearson correlation between TIMFs of different brain areas. Thus, instead of a single connectivity matrix, we obtained 5 x 3 = 15 functional connectivity matrices. Given the high value obtained for large-scale topological measures such as transitivity, in the low spatial maps (SIMF3, SIMF4, and SIMF5), our results suggest that these maps can be considered as spatial global signal masks. Thus, the spatiotemporal EMD of fMRI signals automatically regressed out the GS, although, interestingly, the removed noisy component was voxel-specific. We compared the performance of our method with the conventional GS regression and to the results when the GS was not removed. While the correlation pattern identified by the other methods suffers from a low level of precision, our approach demonstrated a high level of accuracy in extracting the correct correlation between different brain regions.

neuroscience↗