Biology subjects

Brehler, M.

Publications and source records attributed to Brehler, M..

3 recordsLinked to original sources

bioRxiv2024

Podocyte exopher-formation as a novel pathomechanism in membranous nephropathy

BackgroundMembranous nephropathy (MN) is caused by autoantibody binding to podocyte foot process antigens such as THSD7A and PLA2R1. The mechanisms of the glomerular antigen/autoantibody deposition and clearance are unknown. MethodsWe explore the origin and significance of glomerular accumulations in (1) diagnostic and follow-up biospecimens from THSD7A+ and PLA2R1+-MN patients compared to nephrotic non-MN patients, and (2) in experimental models of THSD7A+-MN. ResultsWe discovered podocyte exophers as correlates of histological antigen/autoantibody aggregates found in the glomerular urinary space of MN patients. Exopher vesicle formation represents a novel form of toxic protein aggregate removal in Caenorhabditis elegans neurons. In MN patients, podocytes released exophers to the urine. Enrichment of exophers from MN patient urines established them as a glomerular exit route for antigens and bound autoantibody. Exophers also carried disease-associated proteins such as complement and provided a molecular imprint of podocyte injury pathways. In experimental THSD7A+-MN, exophers were formed from podocyte processes and cell body. Their formation involved the translocation of antigen/autoantibody from the subepithelial to the urinary side of podocyte plasma membranes. Urinary exopher-release correlated with lower albuminuria and lower glomerular antigen/autoantibody burden. In MN patients the prospective monitoring of urinary exopher abundance and of exopher-bound autoantibodies was additive in the assessment of immunologic MN activity. ConclusionsExopher-formation and release is a novel pathomechanism in MN to remove antigen/autoantibody aggregates from the podocyte. Tracking exopher-release will add a non-invasive diagnostic tool with prognostic potential to clinical diagnostics and follow-up of MN patients.

cell biology↗

bioRxiv2022

Comparison of machine learning to deep learning for automated annotation of Gleason patterns in whole mount prostate cancer histology.

BackgroundOne in eight men will be affected by prostate cancer (PCa) in their lives. While the current clinical standard prognostic marker for PCa is the Gleason score, it is subject to interreviewer variability. This study compares two machine learning methods for discriminating between high- and low-grade PCa on histology from 47 PCa patients. MethodsDigitized slides were annotated by a GU fellowship-trained pathologist. High-resolution tiles were extracted from annotated and unlabeled tissue. Glands were segmented and pathomic features were calculated and averaged across each patient. Patients were separated into a training set of 31 patients (Cohort A, n=9345 tiles) and a testing cohort of 16 patients (Cohort B, n=4375 tiles). Tiles from Cohort A were used to train a compact classification ensemble model and a ResNet model to discriminate tumor and were compared to pathologist annotations. ResultsThe ensemble and ResNet models had overall accuracies of 89% and 88%, respectively. The ResNet model was additionally able to differentiate Gleason patterns on data from Cohort B while the ensemble model was not. ConclusionsOur results suggest that quantitative pathomic features calculated from PCa histology can distinguish regions of cancer; how-ever, texture features captured by deep learning frameworks better differentiate unique Gleason patterns.

pathology↗

bioRxiv2021

Radio-pathomic maps of cell density identify glioma invasion beyond traditional MR imaging defined margins

Current MRI signatures of brain cancer often fail to identify regions of hypercellularity beyond the contrast enhancing region. Therefore, this study used autopsy tissue samples aligned to clinical MRIs in order to quantify the relationship between intensity values and cellularity, as well as to develop a radio-pathomic model to predict cellularity using MRI data. This study used 93 samples collected at autopsy from 44 brain cancer patients. Tissue samples were processed, stained for hematoxylin and eosin (HE) and digitized for nuclei segmentation and cell density calculation. Pre- and post-gadolinium contrast T1-weighted images (T1, T1C), T2 fluid-attenuated inversion recovery (FLAIR) images, and apparent diffusion coefficient (ADC) images calculated from diffusion imaging were collected from each patients final acquisition prior to death. In-house software was used to align tissue samples to the FLAIR image via manually defined control points. Mixed effect models were used to assess the relationship between single image intensity and cellularity for each image. An ensemble learner was trained to predict cellularity using 5 by 5 voxel tiles from each image, employing a 2/3-1/3 train-test split for validation. Single image analyses found subtle associations between image intensity and cellularity, with a less pronounced relationship within GBM patients. The radio-pathomic model was able to accurately predict cellularity in the test set (RMSE = 1015 cells/mm2) and identified regions of hypercellularity beyond the contrast enhancing region. We concluded that a radio-pathomic model for cellularity is able to identify regions of hypercellular tumor beyond traditional imaging signatures.

neuroscience↗