Biology subjects

Ning, L.

Publications and source records attributed to Ning, L..

23 records · Page 2Linked to original sources

bioRxiv2021

Circulating osteopontin released by injured kidneys causes pulmonary inflammation and edema

Multiorgan failure is devastating, and its mechanisms and mediators are not clear. Tissue injury in one organ appears to trigger disease in remote organs. Kidney and lung are frequently affected, such as when acute kidney injury (AKI) causes acute lung injury (ALI), a frequent clinical condition with high mortality. Here we identify factors secreted from the injured kidney that cause acute lung injury. We developed a murine model mimicking the generation of respiratory failure following acute kidney injury. To identify interorgan crosstalk mediators involved, we performed scRNAseq of mouse kidneys and lungs after AKI. We then applied ligand-receptor (L-R) pairing analysis across cells residing in kidney (ligands) or lung (receptors) to identify kidney-released circulating osteopontin (OPN) as a novel mediator of AKI-induced ALI (AKI-ALI). OPN release very early after AKI largely from tubule cells triggered neutrophil and macrophage infiltration into lungs associated with endothelial leakage, interstitial edema, and functional impairment. Pharmacological or genetic inhibition of OPN prevented AKI-ALI. Transplantation of ischemic wt kidneys into wt mice caused AKI-ALI, while transplantation of ischemic OPN-global-knockout kidneys failed to induce lung endothelial leakage and AKI-ALI, identifying circulating kidney-released OPN as sufficient to cause AKI-ALI in vivo. We show that AKI in humans results in elevations in OPN levels in the serum. Increased serum OPN levels in patients with multiorgan failure have been shown to positively correlate with reduced kidney function, respiratory failure, and mortality. Thus, our results identifying OPN as a mediator of AKI-ALI may have important therapeutic implications in human AKI-ALI and multiorgan failure.

cell biology↗

bioRxiv2021

Optical regulation of endogenous RhoA reveals switching of cellular responses by signal amplitude

How protein signaling networks respond to different input strengths is an important but poorly understood problem in cell biology. For example, the small GTPase RhoA regulates both focal adhesion (FA) growth or disassembly, but whether RhoA serves as a switch selecting between cellular outcomes, or if outcomes are simply modulated by additional factors in the cell, is not clear. Here, we develop a photoswitchable RhoA guanine exchange factor, psRhoGEF, to precisely control endogenous RhoA activity. We also develop a FRET-based biosensor to allow visualization of RhoA activity together with psRhoGEF control. Using these new optical tools, we discover that low levels of RhoA activation preferentially induce FA disassembly in a Src-dependent manner, while high levels induce both FA growth and disassembly in a ROCK-dependent manner. Thus, rheostatic control of RhoA activation with photoswitchable RhoGEF reveals that cells can use signal amplitude to produce multiple responses to a single biochemical signal.

bioengineering↗

bioRxiv2020

Deep Learning Based Segmentation of Brain Tissue from Diffusion MRI

Segmentation of brain tissue types from diffusion MRI (dMRI) is an important task, required for quantification of brain microstructure and for improving tractography. Current dMRI segmentation is mostly based on anatomical MRI (e.g., T1- and T2-weighted) segmentation that is registered to the dMRI space. However, such inter-modality registration is challenging due to more image distortions and lower image resolution in the dMRI data as compared with the anatomical MRI data. In this study, we present a deep learning method that learns tissue segmentation from high-quality imaging datasets from the Human Connectome Project (HCP), where registration of anatomical data to dMRI is more precise. The method is then able to predict a tissue segmentation directly from new dMRI data, including data collected with a different acquisition protocol, without requiring anatomical data and inter-modality registration. We train a convolutional neural network (CNN) to learn a tissue segmentation model using a novel augmented target loss function designed to improve accuracy in regions of tissue boundary. To further improve accuracy, our method adds diffusion kurtosis imaging (DKI) parameters that characterize non-Gaussian water molecule diffusion to the conventional diffusion tensor imaging parameters. The DKI parameters are calculated from the recently proposed mean-kurtosis-curve method that corrects implausible DKI parameter values and provides additional features that discriminate between tissue types. We demonstrate high tissue segmentation accuracy on HCP data, and also when applying the HCP-trained model on dMRI data from a clinical acquisition with lower resolution and fewer gradient directions.

neuroscience↗

bioRxiv2019

Joint RElaxation-Diffusion Imaging Moments (REDIM) to probe neurite microstructure

Joint relaxation-diffusion measurements can provide new insight about the tissue microstructural properties. Most recent methods have focused on inverting the Laplace transform to recover the joint distribution of relaxation-diffusion. However, as is well-known, this problem is notoriously ill-posed and numerically unstable. In this work, we address this issue by directly computing the joint moments of transverse relaxation rate and diffusivity, which can be robustly estimated. To zoom into different parts of the joint distribution, we further enhance our method by applying multiplicative filters to the joint probability density function of relaxation and diffusion and compute the corresponding moments. We propose an approach to use these moments to compute several novel scalar indices to characterize specific properties of the underlying tissue microstructure. Furthermore, for the first time, we propose an algorithm to estimate diffusion signals that are independent of echo time based on the moments of the marginal probability density function of diffusion. We demonstrate its utility in extracting tissue information not contaminated with multiple intra-voxel relaxation rates. We compare the performance of four types of filters that zoom into tissue components with different relaxation and diffusion properties and demonstrate it on an in-vivo human dataset. Experimental results show that these filters are able to characterize heterogeneous tissue microstructure. Moreover, the filtered diffusion signals are also able to distinguish fiber bundles with similar orientations but different relaxation rates. The proposed method thus allows to characterize the neural microstructure information in a robust and unique manner not possible using existing techniques.

neuroscience↗

bioRxiv2018

Retrospective harmonization of multi-site diffusion MRI data acquired with different acquisition parameters

A joint and integrated analysis of multi-site diffusion MRI (dMRI) datasets can dramatically increase the statistical power of neuroimaging studies and enable comparative studies pertaining to several brain disorders. However, dMRI data sets acquired on multiple scanners cannot be naively pooled for joint analysis due to scanner specific nonlinear effects as well as differences in acquisition parameters. Consequently, for joint analysis, the dMRI data has to be harmonized, which involves removing scanner-specific differences from the raw dMRI signal. In this work, we present a dMRI harmonization method that, when applied to multi-site data, is capable of removing scanner-specific effects, while accounting for minor differences in acquisition parameters such as b-value, spatial resolution and number of gradient directions in the dMRI data (typical for multi-site clinical research scans). We validate our algorithm on dMRI data acquired from two sites: Philadelphia Neurodevelopmental Cohort (PNC) with 800 healthy adolescents (ages 8 to 22 years) and Brigham and Womens Hospital (BWH) with 70 healthy subjects (ages 14 to 54 years). In particular, we show that gender differences and maturation in different age groups are preserved after harmonization, as measured using effect sizes (small, medium and large), irrespective of the test sample size. Further, because we use matched control subjects from different scanners to estimate scanner-specific effects, we tested how many subjects are needed from each site to achieve best harmonization results. Our results indicate that at-least 16 to 18 well-matched healthy controls from each site are needed to reliably capture scanner related differences. The proposed method can thus be used for retrospective harmonization of raw dMRI data across sites despite differences in acquisition parameters, while preserving inter-subject anatomical variability.

neuroscience↗