Biology subjects

Ouyang, M.

Publications and source records attributed to Ouyang, M..

21 records · Page 2Linked to original sources

bioRxiv2019

Sensing Traction Force on Matrix Induces Cell-Cell Distant Mechanical Communications for Self-assembly

The long-range biomechanical force propagating across large scale may reserve the capability to trigger coordinative responses within cell population such as during angiogenesis, epithelial tubulogenesis, and cancer metastasis. How cells communicate in a distant manner within the group for self-assembly remains largely unknown. Here we found that airway smooth muscle cells (ASMCs) rapidly self-assembled into well-constructed network on 3D Matrigel containing type I collagen (COL), which relied on long-range biomechanical force across the matrix to direct cell-cell distant interactions. Similar results happened by HUVEC cells to mimic angiogenesis. Interestingly, single ASMCs initiated multiple extended protrusions precisely pointing to neighboring cells in distance, depending on traction force sensing. Separate ASMCs sensed each other to move directionally on both non-fibrous Matrigel and more efficiently when containing fibrous COL, but lost mutual sensing on fixed gel or coated glass due to no long-range force transmission. Beads tracking assay demonstrated distant transmission of traction force, and finite element method modeling confirmed the consistency between maximum strain distribution on matrix and cell directional movements in experiments. Furthermore, ASMCs recruited COL from the hydrogel to build fibrous network to mechanically stabilize cell network. Our results revealed for the first time that cells can sense traction force transmitted through the matrix to initiate cell-cell distant mechanical communications, resulting in cell directional migration and coordinative self-assembly with active matrix remodeling. As an interesting phenomenon, cells sound able to make phone call via long-range biomechanics, which implicates physiological importance such as for tissue pattern formation.

biophysics↗

bioRxiv2019

Regularized-Ncut: Robust and homogeneous functional parcellation of neonate and adult brain networks

Brain network parcellation based on resting-state functional MRI (rs-fMRI) is affected by noise, resulting in spurious small patches and decreased functional homogeneity within each network. Robust and homogeneous parcellation of neonate brain is more difficult with neonate rs-fMRI associated with higher level of noise and no functional atlas as spatial constraints. To meet these challenges, we developed a novel data-driven Regularized Normalized-cut (RNcut) method. RNcut is formulated by adding two regularization terms, a smoothing term using Markov random fields and a small-patch removal term, to conventional normalized-cut (Ncut) method. The RNcut and competing methods were tested with simulated datasets with known ground truth and then applied to both adult and neonate rs-fMRI datasets. Based on the parcellated networks generated by RNcut, intra-network connectivity was quantified. The test results from simulated datasets demonstrated that the RNcut method is more robust (p<0.01) to noise and can delineate parcellated functional networks with significantly better (p<0.01) spatial contiguity and significantly higher (p<0.01) functional homogeneity than competing methods. Application of RNcut to neonate and adult rs-fMRI dataset revealed distinctive functional brain organization of neonate brains from that of adult brains. Collectively, we developed a novel data-driven RNcut method by integrating conventional Ncut with two regularization terms, generating robust and homogeneous functional parcellation without imposing spatial constraints. A broad range of brain network applications and analyses, especially exploratory investigations of parcellating neonate and infant brain with noisy dataset, can potentially benefit from this RNcut method.

neuroscience↗

bioRxiv2017

Structural network maturation of the preterm human brain

During the 3rd trimester, large-scale of neural circuits are formed in the human brain, resulting in the adult-like brain networks at birth. However, how the brain circuits develop into a highly efficient and segregated connectome during this period is unknown. We hypothesized that faster increases of connectivity efficiency and strength at the brain hubs and rich-club are critical for emergence of an efficient and segregated brain connectome. Here, using high resolution diffusion MRI of 77 preterm-born and term-born neonates scanned at 31-42 postmenstrual weeks (PMW), we constructed the structural connectivity matrices and performed graph-theory-based analyses. We found faster increases of nodal efficiency mainly at the brain hubs, distributed in primary sensorimotor regions, superior-middle frontal and posterior cingulate gyrus during 31-42PMW. The rich-club and within-module connections were characterized by higher rates of edge strength increases. Edge strength of short-range connections increased faster than that of long-range connections. The nodal efficiencies of the hubs predicted individual postmenstrual ages more accurately than those of non-hubs. Collectively, these findings revealed regionally differentiated maturation in the baby brain structural connectome and more rapid increases of the hub and rich-club connections, which underlie network segregation and differentiated brain function emergence.

neuroscience↗