| 1. |
- Hermsen, Meyke, et al.
(author)
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Quantitative assessment of inflammatory infiltrates in kidney transplant biopsies using multiplex tyramide signal amplification and deep learning
- 2021
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In: Laboratory Investigation. - : Springer Nature. - 0023-6837 .- 1530-0307. ; 101:8, s. 970-982
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Journal article (peer-reviewed)abstract
- Delayed graft function (DGF) is a strong risk factor for development of interstitial fibrosis and tubular atrophy (IFTA) in kidney transplants. Quantitative assessment of inflammatory infiltrates in kidney biopsies of DGF patients can reveal predictive markers for IFTA development. In this study, we combined multiplex tyramide signal amplification (mTSA) and convolutional neural networks (CNNs) to assess the inflammatory microenvironment in kidney biopsies of DGF patients (n = 22) taken at 6 weeks post-transplantation. Patients were stratified for IFTA development (<10% versus >= 10%) from 6 weeks to 6 months post-transplantation, based on histopathological assessment by three kidney pathologists. One mTSA panel was developed for visualization of capillaries, T- and B-lymphocytes and macrophages and a second mTSA panel for T-helper cell and macrophage subsets. The slides were multi spectrally imaged and custom-made python scripts enabled conversion to artificial brightfield whole-slide images (WSI). We used an existing CNN for the detection of lymphocytes with cytoplasmatic staining patterns in immunohistochemistry and developed two new CNNs for the detection of macrophages and nuclear-stained lymphocytes. F1-scores were 0.77 (nuclear-stained lymphocytes), 0.81 (cytoplasmatic-stained lymphocytes), and 0.82 (macrophages) on a test set of artificial brightfield WSI. The CNNs were used to detect inflammatory cells, after which we assessed the peritubular capillary extent, cell density, cell ratios, and cell distance in the two patient groups. In this cohort, distance of macrophages to other immune cells and peritubular capillary extent did not vary significantly at 6 weeks post-transplantation between patient groups. CD163(+) cell density was higher in patients with >= 10% IFTA development 6 months post-transplantation (p < 0.05). CD3(+)CD8(-)/CD3(+)CD8(+) ratios were higher in patients with <10% IFTA development (p < 0.05). We observed a high correlation between CD163(+) and CD4(+)GATA3(+) cell density (R = 0.74, p < 0.001). Our study demonstrates that CNNs can be used to leverage reliable, quantitative results from mTSA-stained, multi spectrally imaged slides of kidney transplant biopsies. This study describes a methodology to assess the microenvironment in sparse tissue samples. Deep learning, multiplex immunohistochemistry, and mathematical image processing techniques were incorporated to quantify lymphocytes, macrophages, and capillaries in kidney transplant biopsies of delayed graft function patients. The quantitative results were used to assess correlations with development of interstitial fibrosis and tubular atrophy.
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| 2. |
- Metzger, Jennifer, et al.
(author)
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Predicting Structural and Functional Properties of Membrane Proteins from Protein Sequence
- 2011
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In: Annual Reports in Computational Chemistry. - Amsterdam : Elsevier Science BV. - 9780444543028 ; , s. 39-64
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Book chapter (peer-reviewed)abstract
- Integral transmembrane (TM) proteins are essential constituents of biological membranes where they fulfill a variety of important cellular functions. Because of difficulties with determining their structures by experimental techniques, comparably few 3D structures of membrane proteins are known so far. Therefore, computational methods trained on the available structures using only the protein sequence as input have become important tools in this field. In this chapter, we give a short introduction to the topic and then summarize recent bioinformatics tools for the prediction of structural as well as functional properties of alpha-helical and beta-barrel TM proteins. We present methods that allow predicting the locations of alpha-helical and beta-strand TM segments, to determine the exposure status of residues in the TM region to the surrounding lipids, and that allow functional annotations from the protein sequence.
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