Specific in situ inflammatory states associate with progression to renal failure in lupus nephritis
Rebecca Abraham, Madeleine S. Durkee, Junting Ai, Margaret Veselits, Gabriel Casella, Yuta Asano, Anthony Chang, Kichul Ko, Charles Oshinsky, Emily Peninger, Maryellen L. Giger, Marcus R. Clark
Rebecca Abraham, Madeleine S. Durkee, Junting Ai, Margaret Veselits, Gabriel Casella, Yuta Asano, Anthony Chang, Kichul Ko, Charles Oshinsky, Emily Peninger, Maryellen L. Giger, Marcus R. Clark
View: Text | PDF
Clinical Research and Public Health Autoimmunity

Specific in situ inflammatory states associate with progression to renal failure in lupus nephritis

Abstract

BACKGROUND In human lupus nephritis (LN), tubulointerstitial inflammation (TII) on biopsy predicts progression to end-stage renal disease (ESRD). However, only about half of patients with moderate-to-severe TII develop ESRD. We hypothesized that this heterogeneity in outcome reflects different underlying inflammatory states. Therefore, we interrogated renal biopsies from LN longitudinal and cross-sectional cohorts.METHODS Data were acquired using conventional and highly multiplexed confocal microscopy. To accurately segment cells across whole biopsies, and to understand their spatial relationships, we developed computational pipelines by training and implementing several deep-learning models and other computer vision techniques.RESULTS High B cell densities were associated with protection from ESRD. In contrast, high densities of CD8+, γδ, and other CD4–CD8– T cells were associated with both acute renal failure and progression to ESRD. B cells were often organized into large periglomerular neighborhoods with Tfh cells, while CD4– T cells formed small neighborhoods in the tubulointerstitium, with frequency that predicted progression to ESRD.CONCLUSION These data reveal that specific in situ inflammatory states are associated with refractory and progressive renal disease.FUNDING This study was funded by the NIH Autoimmunity Centers of Excellence (AI082724), Department of Defense (LRI180083), Alliance for Lupus Research, and NIH awards (S10-OD025081, S10-RR021039, and P30-CA14599).

Authors

Rebecca Abraham, Madeleine S. Durkee, Junting Ai, Margaret Veselits, Gabriel Casella, Yuta Asano, Anthony Chang, Kichul Ko, Charles Oshinsky, Emily Peninger, Maryellen L. Giger, Marcus R. Clark

×

Figure 1

Instance segmentation of immune cells in high-resolution fluorescence microscopy images of LN kidney biopsies.

Options: View larger image (or click on image) Download as PowerPoint
Instance segmentation of immune cells in high-resolution fluorescence mi...
(A) Automatic instance segmentation of 5 immune cell classes was performed by combining predictions from 2 instances of Mask R-CNN: one trained to segment CD20+, CD3+CD4–, and CD3+CD4+ lymphocytes and one trained to segment pDCs and mDCs. Cell location, class, and morphological features were calculated from joint predictions. (B) The Mask R-CNN architecture comprises a ResNet Feature Pyramid Network (FPN) backbone used for feature extraction, a region proposal network (RPN) used to generate cell proposals, and two parallel branches used for (a) semantic segmentation (mask branch) and (b) classification (softmax layer) and localization (bounding box [Bbox] regression) of cell proposals. (C) Representative segmentations produced by the multinetwork pipeline showed strong agreement with the expert-defined manual segmentations. Magnification for all images 63x (zoom factor 1.7). This figure was created with BioRender.com.