Licht, C. | Search bioRxiv

bioRxiv2026

Abundant Glomerular Neutrophil Extracellular Traps in C3 Glomerulopathy

Introduction: C3 glomerulopathy (C3G) is driven by fluid-phase alternative complement pathway dysregulation, with emerging evidence linking glomerular neutrophil infiltration to disease severity. Neutrophil extracellular traps (NETs) are implicated in other forms of glomerulonephritis. However, their participation in the pathogenesis of C3G remains undefined. Methods: Kidney biopsies from 33 patients with C3G (15 with dense deposit disease [DDD] and 18 with C3 glomerulonephritis [C3GN]) were compared with 15 anti-neutrophil cytoplasmic antibody associated vasculitis (AAV) biopsies as a neutrophil-rich disease control in this retrospective cross-sectional study. Glomerular neutrophils and NETs were identified using immunofluorescence, staining for myeloperoxidase, citrullinated histone H3, peptidyl arginine deiminase-4, and DNA. Supervised machine learning was used to quantify glomerular NET formation, and the data were correlated with kidney function at time of biopsy using linear regression. Results: Intraglomerular NETs were abundant and detected in the majority of glomeruli in C3G biopsies. Compared with AAV, C3G showed a significantly higher fraction of neutrophils forming NETs, despite similar neutrophil counts per glomerulus. NET abundance was similar in DDD and C3GN. In exploratory analyses, a greater proportion of glomeruli containing NETs was associated with lower kidney function (estimated glomerular filtration rate) at biopsy, and this association remained significant after adjustment for age, C3G subtype, and interstitial fibrosis. Conclusions: These observations demonstrate that intraglomerular NETs are a common and prominent observation in C3G and are associated with reduced kidney function at biopsy. These findings raise the possibility that NET deposition in glomeruli is a previously unrecognized driver of glomerular injury in C3G.

immunology↗

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↗